Fairness | Papers With Code

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style="padding-top:10px;"><span class=" icon-wrapper icon-fa icon-fa-solid" data-name="edit"><svg viewBox="0 0 576 514.999" xmlns="http://www.w3.org/2000/svg"><path d="M402.6 85.198l90.2 90.2c3.8 3.8 3.8 10 0 13.8l-218.399 218.4-92.8 10.3c-12.4 1.4-22.9-9.1-21.5-21.5l10.3-92.8 218.4-218.4c3.799-3.8 10-3.8 13.799 0zm162-22.9c15.2 15.2 15.2 39.9 0 55.2l-35.4 35.4c-3.8 3.8-10 3.8-13.8 0l-90.2-90.2c-3.8-3.8-3.8-10 0-13.8l35.4-35.4c15.3-15.2 40-15.2 55.2 0zM384 348.198c0-3.2 1.3-6.2 3.5-8.5l40-40c7.6-7.5 20.5-2.2 20.5 8.5v157.8c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48v-352c0-26.5 21.5-48 48-48h285.8c10.7 0 16.1 12.9 8.5 20.5l-40 40c-2.3 2.2-5.3 3.5-8.5 3.5H64v320h320v-101.8z"/></svg></span> Edit</span> </a> </div> </div> <h1 id="task-home">Fairness</h1> <div class="artefact-information"> <p> 1477 papers with code • 9 benchmarks • 23 datasets </p> </div> </div> <div class="col-lg-10">  <div class="description"> <div class="description-content"> This task has no description! <a style="text-decoration: underline !important;" href="" data-bs-toggle="modal" data-bs-target="#loginModal">Would you like to contribute one?</a> </div> </div>   <div class="task-benchmarks"> <div id="benchmarks" class="collapsed"> <div class="title"> <h2 id="benchmarks">Benchmarks <div class="float-right"> <div class="dropdown edit-button task-add-a-result"> <a data-bs-toggle="modal" data-bs-target="#loginModal"> <span class="badge badge-primary" style="font-size:12px;"> Add a Result</span> </a> </div> </div> </h2> These leaderboards are used to track progress in Fairness <hr> </div> <div class="sota-table-preview table-responsive"> <table id="benchmarksTable" class="table-striped table-responsive"> <thead> <tr> <th>Trend</th> <th style="padding-left:12px;">Dataset</th> <th style="min-width:200px">Best Model</th>  <th class="text-center">Paper</th> <th class="text-center">Code</th> <th class="text-center">Compare</th> </tr> </thead> <tbody> <tr onclick="window.location='/sota/fairness-on-baf-base';"> <td> <a href="/sota/fairness-on-baf-base"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/fairness-on-baf-base-small_b3185fa0.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/fairness-on-baf-base"> BAF – Base </a> </div> </td> <td> <div class="black-links"> <a href="/sota/fairness-on-baf-base"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> 1D-CSNN </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/improving-fraud-detection-with-1d"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/improving-fraud-detection-with-1d#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/fairness-on-baf-base" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/fairness-on-diveface';"> <td> <a href="/sota/fairness-on-diveface"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/fairness-on-diveface-small_3c10a278.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/fairness-on-diveface"> DiveFace </a> </div> </td> <td> <div class="black-links"> <a href="/sota/fairness-on-diveface"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> Neighbour Learning </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/deep-generative-views-to-mitigate-gender"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/fairness-on-diveface" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/fairness-on-utkface';"> <td> <a href="/sota/fairness-on-utkface"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/fairness-on-utkface-small_5e18e058.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/fairness-on-utkface"> UTKFace </a> </div> </td> <td> <div class="black-links"> <a href="/sota/fairness-on-utkface"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> Neighbour Learning </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/deep-generative-views-to-mitigate-gender"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/fairness-on-utkface" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/fairness-on-morph';"> <td> <a href="/sota/fairness-on-morph"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/fairness-on-morph-small_8a5792f1.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/fairness-on-morph"> MORPH </a> </div> </td> <td> <div class="black-links"> <a href="/sota/fairness-on-morph"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> Neighbour Learning </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/deep-generative-views-to-mitigate-gender"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/fairness-on-morph" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/fairness-on-baf-variant-i';"> <td> <a href="/sota/fairness-on-baf-variant-i"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/fairness-on-baf-variant-i-small_9c161f1e.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/fairness-on-baf-variant-i"> BAF – Variant I </a> </div> </td> <td> <div class="black-links"> <a href="/sota/fairness-on-baf-variant-i"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> 1D-CSNN </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/exploring-neural-joint-activity-in-spiking"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/exploring-neural-joint-activity-in-spiking#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/fairness-on-baf-variant-i" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/fairness-on-baf-variant-ii';"> <td> <a href="/sota/fairness-on-baf-variant-ii"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/fairness-on-baf-variant-ii-small_864257cf.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/fairness-on-baf-variant-ii"> BAF – Variant II </a> </div> </td> <td> <div class="black-links"> <a href="/sota/fairness-on-baf-variant-ii"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> 1D-CSNN </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/exploring-neural-joint-activity-in-spiking"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/exploring-neural-joint-activity-in-spiking#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/fairness-on-baf-variant-ii" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/fairness-on-baf-variant-iii';"> <td> <a href="/sota/fairness-on-baf-variant-iii"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/fairness-on-baf-variant-iii-small_3779b99c.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/fairness-on-baf-variant-iii"> BAF – Variant III </a> </div> </td> <td> <div class="black-links"> <a href="/sota/fairness-on-baf-variant-iii"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> 1D-CSNN </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/exploring-neural-joint-activity-in-spiking"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/exploring-neural-joint-activity-in-spiking#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/fairness-on-baf-variant-iii" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/fairness-on-baf-variant-iv';"> <td> <a href="/sota/fairness-on-baf-variant-iv"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/fairness-on-baf-variant-iv-small_0149a4c1.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/fairness-on-baf-variant-iv"> BAF – Variant IV </a> </div> </td> <td> <div class="black-links"> <a href="/sota/fairness-on-baf-variant-iv"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> 1D-CSNN </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/exploring-neural-joint-activity-in-spiking"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> </a> </div> </td> <td> <div class="text-center github"><a href="/paper/exploring-neural-joint-activity-in-spiking#code"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/fairness-on-baf-variant-iv" class="btn btn-primary">See all</a> </div> </td> </tr> <tr onclick="window.location='/sota/fairness-on-baf-variant-v';"> <td> <a href="/sota/fairness-on-baf-variant-v"> <img class="sota-thumb" src="https://production-media.paperswithcode.com/sota-thumbs/fairness-on-baf-variant-v-small_69ca881d.png"/> </a> </td> <td> <div class="dataset black-links"> <a href="/sota/fairness-on-baf-variant-v"> BAF – Variant V </a> </div> </td> <td> <div class="black-links"> <a href="/sota/fairness-on-baf-variant-v"> <i class="em em-trophy" style="height:1em;position:relative;top:-2px"></i> 1D-CSNN </a> </div> </td>  <td> <div class="text-center paper"> <a href="/paper/exploring-neural-joint-activity-in-spiking"> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" 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23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> </a></div> </td> <td class="text-center"> <div class="sota-table-link"> <a href="/sota/fairness-on-baf-variant-v" class="btn btn-primary">See all</a> </div> </td> </tr> </tbody> </table> </div> </div> </div>  <div class="title"> <h2 id="datasets">Datasets</h2> <hr> <div class="task-datasets"> <div class="col-md-12"> <ul class="list-unstyled"> <li> <a href="/dataset/netflix-prize"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000003694-60936df7_370B5WT.jpg"> Netflix Prize </span> </a> </li> <li> <a href="/dataset/utkface"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000710-ea40a961_PpZZLu2.jpg"> UTKFace </span> </a> </li> <li> <a href="/dataset/fairface"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> FairFace </span> </a> </li> <li> <a href="/dataset/morph"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000000550-4597183e_P601veL.jpg"> MORPH </span> </a> </li> <li> <a href="/dataset/winobias"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000003638-1a926642_tNHAOVT.jpg"> WinoBias </span> </a> </li> <li> <a href="/dataset/rfw"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> RFW </span> </a> </li> <li> <a href="/dataset/gvgai"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000003612-71ac7101_CjttmDd.jpg"> GVGAI </span> </a> </li> <li> <a href="/dataset/help"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000004708-29bc2348_WZjdrw5.jpg"> HELP </span> </a> </li> <li> <a href="/dataset/miap"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/dataset/dataset-0000007228-e093de7b.jpg"> MIAP </span> </a> </li> <li> <a href="/dataset/diveface"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> DiveFace </span> </a> </li> <a href="/datasets?task=fairness"> <button class="dropdown-toggle badge badge-edit w-100 collapsed" type="button" > See all 23 fairness datasets </button> </a> </ul> </div> </div> </div>  <div class="title"> <h2 id="subtasks">Subtasks</h2> <hr> <div class="task-subtasks"> <div class="col-md-12"> <ul class="list-unstyled"> <li> <a href="/task/exposure-fairness"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Exposure Fairness</span> </span> </a> </li> </ul> </div> </div> </div>  <div class="title paper-list" id="code"> <h2 id="papers-list" class="home-page-title">Most implemented papers</h2> <div class="paper-filter-btn"> <div class="btn-group" role="group"> <a data-title="Most implemented papers" data-call-url="/tasklist/fairness/greatest" data-target="/task/fairness" class="list-papers-button list-button-active" style="margin-right:0">Most implemented</a> <a data-title="Hot papers on social media" data-call-url="/tasklist/fairness/social" data-target="/task/fairness/social" class="list-papers-button list-button" style="margin-right:0">Social</a> <a data-title="Latest papers" data-call-url="/tasklist/fairness/latest" data-target="/task/fairness/latest" class="list-papers-button list-button" style="margin-right:0">Latest</a> <a data-title="Latest papers with no code" data-call-url="/tasklist/fairness/codeless" data-target="/task/fairness/codeless" class="list-papers-button list-button">No code</a> </div> </div> </div>  <input id="paper-list-search" type="search" class="form-control form-control-sm" placeholder="Search for a paper, author or keyword"> <div class="loading-tab" style="display: none"> <div class="loader-ellips"> <span class="loader-ellips__dot"></span> <span class="loader-ellips__dot"></span> <span class="loader-ellips__dot"></span> <span class="loader-ellips__dot"></span> </div> </div> <div id="task-papers-list"> <div class="infinite-container text-center"> <div class="paper-card infinite-item">  <div class="container-fluid"> <div class="row"> <div class="col-lg-3"> <a href="/paper/a-simple-baseline-for-multi-object-tracking"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2004.01888.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/a-simple-baseline-for-multi-object-tracking">FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/ifzhang/FairMOT" onclick="captureOutboundLink('https://github.com/ifzhang/FairMOT'); return true;" style="font-size:13px"> ifzhang/FairMOT </a> </span> • <span class="item-framework-link"> <img class="" src="https://production-assets.paperswithcode.com/perf/images/frameworks/pytorch-2fbf2cb9.png" /> </span> • <span class="author-name-text item-date-pub">4 Apr 2020</span> </p> <p class="item-strip-abstract">Formulating MOT as multi-task learning of object detection and re-ID in a single network is appealing since it allows joint optimization of the two tasks and enjoys high computation efficiency.</p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> 33</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/a-simple-baseline-for-multi-object-tracking" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/a-simple-baseline-for-multi-object-tracking#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> </div> </div> <div class="paper-card infinite-item">  <div class="container-fluid"> <div class="row"> <div class="col-lg-3"> <a href="/paper/ai-fairness-360-an-extensible-toolkit-for"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1810.01943.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/ai-fairness-360-an-extensible-toolkit-for">AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/IBM/AIF360" onclick="captureOutboundLink('https://github.com/IBM/AIF360'); return true;" style="font-size:13px"> IBM/AIF360 </a> </span> • <span class="item-framework-link"> <img class="" 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/> </span> • <span class="author-name-text item-date-pub">3 Oct 2018</span> </p> <p class="item-strip-abstract">Such architectural design and abstractions enable researchers and developers to extend the toolkit with their new algorithms and improvements, and to use it for performance benchmarking.</p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> 13</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/ai-fairness-360-an-extensible-toolkit-for" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a 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363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> </div> </div> <div class="paper-card infinite-item">  <div class="container-fluid"> <div class="row"> <div class="col-lg-3"> <a href="/paper/score-camimproved-visual-explanations-via"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1910.01279.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/score-camimproved-visual-explanations-via">Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/haofanwang/Score-CAM" onclick="captureOutboundLink('https://github.com/haofanwang/Score-CAM'); return true;" style="font-size:13px"> haofanwang/Score-CAM </a> </span> • <span class="item-framework-link"> <img class="" src="https://production-assets.paperswithcode.com/perf/images/frameworks/pytorch-2fbf2cb9.png" /> </span> • <span class="author-name-text item-date-pub">3 Oct 2019</span> </p> <p class="item-strip-abstract">Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions.</p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> 9</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/score-camimproved-visual-explanations-via" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/score-camimproved-visual-explanations-via#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> </div> </div> <div class="paper-card infinite-item">  <div class="container-fluid"> <div class="row"> <div class="col-lg-3"> <a href="/paper/a-critic-evaluation-of-methods-for-covid-19"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2004.12823.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/a-critic-evaluation-of-methods-for-covid-19">A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/imanpalsingh/COVID-19-Diagnosis-using-Convolutional-and-Generative-models" onclick="captureOutboundLink('https://github.com/imanpalsingh/COVID-19-Diagnosis-using-Convolutional-and-Generative-models'); return true;" style="font-size:13px"> imanpalsingh/COVID-19-Diagnosis-using-Convolutional-and-Generative-models </a> </span> • <span class="item-framework-link"> <img class="" src="data:image/png;base64,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" /> </span> • <span class="author-name-text item-date-pub">27 Apr 2020</span> </p> <p class="item-strip-abstract">In this paper, we compare and evaluate different testing protocols used for automatic COVID-19 diagnosis from X-Ray images in the recent literature.</p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> 9</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/a-critic-evaluation-of-methods-for-covid-19" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/a-critic-evaluation-of-methods-for-covid-19#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> </div> </div> <div class="paper-card infinite-item">  <div class="container-fluid"> <div class="row"> <div class="col-lg-3"> <a href="/paper/elevater-a-benchmark-and-toolkit-for"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2204.08790.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/elevater-a-benchmark-and-toolkit-for">ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/computer-vision-in-the-wild/cvinw_readings" onclick="captureOutboundLink('https://github.com/computer-vision-in-the-wild/cvinw_readings'); return true;" style="font-size:13px"> computer-vision-in-the-wild/cvinw_readings </a> </span> • <span class="author-name-text item-date-pub">19 Apr 2022</span> </p> <p class="item-strip-abstract">In general, these language-augmented visual models demonstrate strong transferability to a variety of datasets and tasks.</p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> 9</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/elevater-a-benchmark-and-toolkit-for" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/elevater-a-benchmark-and-toolkit-for#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> </div> </div> <div class="paper-card infinite-item">  <div class="container-fluid"> <div class="row"> <div class="col-lg-3"> <a href="/paper/learning-adversarially-fair-and-transferable"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1802.06309.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/learning-adversarially-fair-and-transferable">Learning Adversarially Fair and Transferable Representations</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/VectorInstitute/laftr" onclick="captureOutboundLink('https://github.com/VectorInstitute/laftr'); return true;" style="font-size:13px"> VectorInstitute/laftr </a> </span> • <span class="item-conference-link"> <a href="/conference/icml-2018-7"> ICML 2018 </a> </span> </p> <p class="item-strip-abstract">In this paper, we advocate for representation learning as the key to mitigating unfair prediction outcomes downstream.</p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> 7</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/learning-adversarially-fair-and-transferable" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/learning-adversarially-fair-and-transferable#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" 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class="col-lg-3"> <a href="/paper/agnostic-federated-learning"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1902.00146.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/agnostic-federated-learning">Agnostic Federated Learning</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/litian96/fair_flearn" onclick="captureOutboundLink('https://github.com/litian96/fair_flearn'); return true;" style="font-size:13px"> litian96/fair_flearn </a> </span> • <span class="author-name-text item-date-pub">1 Feb 2019</span> </p> <p class="item-strip-abstract">A key learning scenario in large-scale applications is that of federated learning, where a centralized model is trained based on data originating from a large number of clients.</p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 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2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> </div> </div> <div class="paper-card infinite-item">  <div class="container-fluid"> <div class="row"> <div class="col-lg-3"> <a href="/paper/learning-to-pivot-with-adversarial-networks"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1611.01046.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/learning-to-pivot-with-adversarial-networks">Learning to Pivot with Adversarial Networks</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/glouppe/paper-learning-to-pivot" onclick="captureOutboundLink('https://github.com/glouppe/paper-learning-to-pivot'); return true;" style="font-size:13px"> glouppe/paper-learning-to-pivot </a> </span> • <span class="item-conference-link"> <a href="/conference/neurips-2017-12"> NeurIPS 2017 </a> </span> </p> <p class="item-strip-abstract">Several techniques for domain adaptation have been proposed to account for differences in the distribution of the data used for training and testing.</p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> 5</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/learning-to-pivot-with-adversarial-networks" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/learning-to-pivot-with-adversarial-networks#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> </div> </div> <div class="paper-card infinite-item">  <div class="container-fluid"> <div class="row"> <div class="col-lg-3"> <a href="/paper/preventing-fairness-gerrymandering-auditing"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1711.05144.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/preventing-fairness-gerrymandering-auditing">Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/algowatchpenn/GerryFair" onclick="captureOutboundLink('https://github.com/algowatchpenn/GerryFair'); return true;" style="font-size:13px"> algowatchpenn/GerryFair </a> </span> • <span class="item-conference-link"> <a href="/conference/icml-2018-7"> ICML 2018 </a> </span> </p> <p class="item-strip-abstract">We prove that the computational problem of auditing subgroup fairness for both equality of false positive rates and statistical parity is equivalent to the problem of weak agnostic learning, which means it is computationally hard in the worst case, even for simple structured subclasses.</p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> 5</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/preventing-fairness-gerrymandering-auditing" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/preventing-fairness-gerrymandering-auditing#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> </div> </div> <div class="paper-card infinite-item">  <div class="container-fluid"> <div class="row"> <div class="col-lg-3"> <a href="/paper/an-empirical-study-of-rich-subgroup-fairness"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1808.08166.jpg');"> </div> </a> </div> <div class="col-lg-9"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/an-empirical-study-of-rich-subgroup-fairness">An Empirical Study of Rich Subgroup Fairness for Machine Learning</a></h1> <p class="author-section" style="padding-top:2px"> <span class="item-github-link"> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> <a href="https://github.com/algowatchpenn/GerryFair" onclick="captureOutboundLink('https://github.com/algowatchpenn/GerryFair'); return true;" style="font-size:13px"> algowatchpenn/GerryFair </a> </span> • <span class="author-name-text item-date-pub">24 Aug 2018</span> </p> <p class="item-strip-abstract">In this paper, we undertake an extensive empirical evaluation of the algorithm of Kearns et al. On four real datasets for which fairness is a concern, we investigate the basic convergence of the algorithm when instantiated with fast heuristics in place of learning oracles, measure the tradeoffs between fairness and accuracy, and compare this approach with the recent algorithm of Agarwal et al. [2018], which implements weaker and more traditional marginal fairness constraints defined by individual protected attributes.</p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> 5</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/an-empirical-study-of-rich-subgroup-fairness" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/an-empirical-study-of-rich-subgroup-fairness#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" 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