Alexey Kurakin

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href="http://research.google/research-areas/speech-processing/" class="navigation__sub__columns__mobile__link" > Speech Processing <span> </span> </a> </li> </ul> </div> </div> </li> <li data-gt-secondary="Computing Systems & Quantum AI"> <div class="navigation__sub__columns__desktop"> <h2 class="headline-6 navigation__sub__columns__heading">Computing Systems & Quantum AI</h2> <ul> <li> <a class="navigation__sub__columns__list-link caption js-drawer-link" href="http://research.google/research-areas/distributed-systems-and-parallel-computing/" > Distributed Systems & Parallel Computing </a> </li> <li> <a class="navigation__sub__columns__list-link caption js-drawer-link" href="http://research.google/research-areas/hardware-and-architecture/" > Hardware & Architecture </a> </li> <li> <a class="navigation__sub__columns__list-link caption js-drawer-link" href="http://research.google/research-areas/mobile-systems/" > Mobile Systems </a> </li> <li> <a 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Software Systems </a> </li> </ul> </div> <div class="navigation__sub__columns__mobile"> <button class="glue-header__link js-sub-nav-target" data-panel="nested" role="menuitem" aria-haspopup="true"> Computing Systems & Quantum AI <span class="icon icon--caret"></span> </button> <div class="navigation__nested-sub js-sub-nav-parent"> <div class="navigation__sub__mobile-heading"> <button class="glue-header__link js-sub-nav-close-mobile" role="menuitem" aria-haspopup="true"> <span class="sr-text">Back to</span> <span class="icon icon--caret"></span> Computing Systems & Quantum AI <span class="sr-text">menu</span> </button> <hr/> </div> <ul> <li role="menuitem"> <a href="http://research.google/research-areas/distributed-systems-and-parallel-computing/" class="navigation__sub__columns__mobile__link" > Distributed Systems & Parallel Computing <span> </span> </a> </li> <li role="menuitem"> <a href="http://research.google/research-areas/hardware-and-architecture/" 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<svg class="glue-icon glue-icon--24px" role="presentation" aria-hidden="true"> <use href="/gr/static/assets/icons/glue-icons.svg#close"></use> </svg> </button> </div> </div> </header> <main id="page-content"> <div class="person-detail-page"> <section class="basic-hero bhoig --theme-dark " data-gt-id="basic_hero" data-gt-component-name=""> <div class="glue-page"> <div class="glue-grid"> <div class="bhoig__image-wrapper glue-grid__col--span-2 glue-grid__col--span-3-md glue-grid__col--span-2-lg"> <div class="bhoig__image-bg" style=""> <picture> <img src="https://storage.googleapis.com/gweb-research2023-media/pubtools/3501.png" alt="Alexey Kurakin" class=" basic-hero__person-image "/> </picture> </div> </div> <div class="bhoig__breadcrumb-wrapper glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg"> <nav class="glue-breadcrumbs" aria-label="Breadcrumbs"> <ol class="glue-breadcrumbs__list"> <li class="glue-breadcrumbs__item"> <a class="glue-breadcrumbs__link attribution" href="/">Home</a> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#chevron-right"></use> </svg> </li> <li class="glue-breadcrumbs__item"> <a class="glue-breadcrumbs__link attribution" href="/people/">People</a> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#chevron-right"></use> </svg> </li> </ol> </nav> </div> <h1 class="headline-1 bhoig__headline glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg">Alexey Kurakin</h1> <div class="bhoig__cta glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg"> </div> </div> </div> </section> <section class="offset-two-up --research-areas"> <div class="glue-page glue-grid"> <div class="offset-two-up__left-col glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-3-lg"> <h3 class="offset-two-up__headline headline-3">Research Areas</h3> </div> <div class="glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-9-lg"> <ul class="card-stack--small nested-glue-grid-override"> <li class="glue-grid__col glue-grid__col--span-6-md glue-grid__col--span-4-sm"> <a class="glue-card not-glue glue-card--small" href="/research-areas/machine-intelligence/"> <div class="glue-card__inner"> <div class="glue-card__content"> <div class="content-wrapper"> <p class="glue-headline body js-gt-item-id"> Machine Intelligence </p> </div> <div class="glue-card__cta glue-card__cta--arrow"> <span class="glue-button glue-button--low-emphasis glue-button--icon"> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#arrow-forward"></use> </svg> </span> </div> </div> </div> </a> </li> </ul> </div> </div> </section> <div class="glue-page heading-tabs-wrapper spacer-03-top"> <span class="--as-h4">Authored Publications</span> </div>  <section class="publication-index filtered-list-base hot-swap-base glue-page theme-light spacer-06-top spacer-06-bottom"> <div class="js-configurable-list glue-grid" data-list-id="publications-index-list" data-search-query-param=""> <div class="filter-wrapper"> <div class="glue-filter glue-filter--mws" id="my-filter" data-target-id="publications-index-list" data-chipset-id="publications-index-chipset" data-use-facet-names="True" >  <div class="glue-grid__col glue-grid__col--span-3 glue-grid__col--span-12-md">  <button class="glue-button glue-button--low-emphasis glue-button--icon glue-filter__modal-toggle"> See Filters <svg role="presentation" aria-hidden="true" class="glue-icon"> <use href="/gr/static/assets/icons/glue-icons.svg#filter"></use> </svg> </button>  <div class="glue-modal glue-filter__modal" aria-labelledby="modal-label"> <div id="modal-content" class="glue-filter__modal-content"> <output class="glue-filter__results-status glue-visually-hidden" aria-live="polite" aria-atomic="true"> <span class="glue-filter__results-count"></span> results </output> <div class="glue-filter__categories glue-filter__panels glue-expansion-panels js-gt-filter-wrapper" data-glue-expansion-panels-key="filter-mws" data-glue-expansion-panel-expand-tooltip="Expand this filter category" data-glue-expansion-panel-collapse-tooltip="Collapse this filter category">  <div class="glue-expansion-panels__header"> <h2 class="glue-headline caption" id="modal-label"> Filter by: </h2>  <div class="glue-filter__modal-controls glue-filter__filter-controls js-gt-filter-clear"> <button class="glue-button glue-button--low-emphasis glue-filter__reset-all" > <span class="js-gt-item-id">Clear</span> </button> </div> <button class="glue-modal__close-btn" title="Close the modal" tabindex="0" aria-label="Close the modal"> </button> </div>  <div class="glue-expansion-panel filter__first-panel"> <h3 class="glue-expansion-panel__toggle"> <span class="glue-expansion-panel__button" id="filter-toggle-type" data-glue-expansion-panel-toggle-for="filter-panel-type"> <span class="glue-expansion-panel__header-text" id="filter-type-label">Publications</span> <svg role="presentation" aria-hidden="true" class="glue-icon glue-expansion-panel__header-arrow"> <use href="/gr/static/assets/icons/glue-icons.svg#arrow-drop-down"></use> </svg> </span> </h3>  <div class="glue-expansion-panel__content" id="filter-panel-type"> <div> <ul class="glue-filter__category glue-no-bullet" data-glue-filter-category="type" role="list" aria-controls="filter-results" aria-labelledby="filter-type-label"> <li class="glue-filter__category-item js-filter-item js-gt-filter-item"> <div class="mdc-touch-target-wrapper"> <div class="mdc-form-field"> <div class="mdc-checkbox mdc-checkbox--touch glue-checkbox"> <input type="checkbox" class="mdc-checkbox__native-control" id="filter-type-google" name="filter-type-google" value="google" > <div class="mdc-checkbox__background"> <svg class="mdc-checkbox__checkmark" viewBox="0 0 24 24"> <path class="mdc-checkbox__checkmark-path" fill="none" d="M1.73,12.91 8.1,19.28 22.79,4.59"></path> </svg> <div class="mdc-checkbox__mixedmark"></div> </div> <div class="mdc-checkbox__ripple"></div> </div> <label class="filter-option-label" for="filter-type-google"> <span class="js-filter-option-label-text">Google</span> <span class="filter-option-label-count">8</span></label> </div> </div> </li> <li class="glue-filter__category-item js-filter-item js-gt-filter-item"> <div class="mdc-touch-target-wrapper"> <div class="mdc-form-field"> <div class="mdc-checkbox mdc-checkbox--touch glue-checkbox"> <input type="checkbox" class="mdc-checkbox__native-control" id="filter-type-other" name="filter-type-other" value="other" > <div class="mdc-checkbox__background"> <svg class="mdc-checkbox__checkmark" viewBox="0 0 24 24"> <path class="mdc-checkbox__checkmark-path" fill="none" d="M1.73,12.91 8.1,19.28 22.79,4.59"></path> </svg> <div class="mdc-checkbox__mixedmark"></div> </div> <div class="mdc-checkbox__ripple"></div> </div> <label class="filter-option-label" for="filter-type-other"> <span class="js-filter-option-label-text">Other</span> <span class="filter-option-label-count">0</span></label> </div> </div> </li> </ul> </div> </div> </div> <div class="glue-expansion-panel "> <h3 class="glue-expansion-panel__toggle"> <span class="glue-expansion-panel__button" id="filter-toggle-year" data-glue-expansion-panel-toggle-for="filter-panel-year"> <span class="glue-expansion-panel__header-text" id="filter-year-label">Years</span> <svg role="presentation" aria-hidden="true" class="glue-icon glue-expansion-panel__header-arrow"> <use href="/gr/static/assets/icons/glue-icons.svg#arrow-drop-down"></use> </svg> </span> </h3>  <div class="glue-expansion-panel__content" id="filter-panel-year"> <div> <ul class="glue-filter__category glue-no-bullet" data-glue-filter-category="year" role="list" aria-controls="filter-results" aria-labelledby="filter-year-label"> <li class="glue-filter__category-item js-filter-item js-gt-filter-item"> <div class="mdc-touch-target-wrapper"> <div class="mdc-form-field"> <div class="mdc-checkbox mdc-checkbox--touch glue-checkbox"> <input type="checkbox" class="mdc-checkbox__native-control" id="filter-year-2022" name="filter-year-2022" value="2022" > <div class="mdc-checkbox__background"> <svg class="mdc-checkbox__checkmark" viewBox="0 0 24 24"> <path class="mdc-checkbox__checkmark-path" fill="none" d="M1.73,12.91 8.1,19.28 22.79,4.59"></path> </svg> <div class="mdc-checkbox__mixedmark"></div> </div> <div class="mdc-checkbox__ripple"></div> </div> <label class="filter-option-label" for="filter-year-2022"> <span class="js-filter-option-label-text">2022</span> <span class="filter-option-label-count">1</span></label> </div> </div> </li> <li class="glue-filter__category-item js-filter-item js-gt-filter-item"> <div class="mdc-touch-target-wrapper"> <div class="mdc-form-field"> <div class="mdc-checkbox mdc-checkbox--touch glue-checkbox"> <input type="checkbox" class="mdc-checkbox__native-control" id="filter-year-2020" name="filter-year-2020" value="2020" > <div class="mdc-checkbox__background"> <svg class="mdc-checkbox__checkmark" viewBox="0 0 24 24"> <path class="mdc-checkbox__checkmark-path" fill="none" d="M1.73,12.91 8.1,19.28 22.79,4.59"></path> </svg> <div class="mdc-checkbox__mixedmark"></div> </div> <div class="mdc-checkbox__ripple"></div> </div> <label class="filter-option-label" for="filter-year-2020"> <span class="js-filter-option-label-text">2020</span> <span class="filter-option-label-count">2</span></label> </div> </div> </li> <li class="glue-filter__category-item js-filter-item js-gt-filter-item"> <div class="mdc-touch-target-wrapper"> <div class="mdc-form-field"> <div class="mdc-checkbox mdc-checkbox--touch glue-checkbox"> <input type="checkbox" class="mdc-checkbox__native-control" id="filter-year-2018" name="filter-year-2018" value="2018" > <div class="mdc-checkbox__background"> <svg class="mdc-checkbox__checkmark" viewBox="0 0 24 24"> <path class="mdc-checkbox__checkmark-path" fill="none" d="M1.73,12.91 8.1,19.28 22.79,4.59"></path> </svg> <div class="mdc-checkbox__mixedmark"></div> </div> <div class="mdc-checkbox__ripple"></div> </div> <label class="filter-option-label" for="filter-year-2018"> <span class="js-filter-option-label-text">2018</span> <span class="filter-option-label-count">2</span></label> </div> </div> </li> <li class="glue-filter__category-item js-filter-item js-gt-filter-item"> <div class="mdc-touch-target-wrapper"> <div class="mdc-form-field"> <div class="mdc-checkbox mdc-checkbox--touch glue-checkbox"> <input type="checkbox" class="mdc-checkbox__native-control" id="filter-year-2017" name="filter-year-2017" value="2017" > <div class="mdc-checkbox__background"> <svg class="mdc-checkbox__checkmark" viewBox="0 0 24 24"> <path class="mdc-checkbox__checkmark-path" fill="none" d="M1.73,12.91 8.1,19.28 22.79,4.59"></path> </svg> <div class="mdc-checkbox__mixedmark"></div> </div> <div class="mdc-checkbox__ripple"></div> </div> <label class="filter-option-label" for="filter-year-2017"> <span class="js-filter-option-label-text">2017</span> <span class="filter-option-label-count">3</span></label> </div> </div> </li> </ul> </div> </div> </div> <div class="glue-expansion-panel "> <h3 class="glue-expansion-panel__toggle"> <span class="glue-expansion-panel__button" id="filter-toggle-research_area" data-glue-expansion-panel-toggle-for="filter-panel-research_area"> <span class="glue-expansion-panel__header-text" id="filter-research_area-label">Research Areas</span> <svg role="presentation" aria-hidden="true" class="glue-icon glue-expansion-panel__header-arrow"> <use href="/gr/static/assets/icons/glue-icons.svg#arrow-drop-down"></use> </svg> </span> </h3>  <div class="glue-expansion-panel__content" id="filter-panel-research_area"> <div> <ul class="glue-filter__category glue-no-bullet" data-glue-filter-category="research_area" role="list" aria-controls="filter-results" aria-labelledby="filter-research_area-label"> <li class="glue-filter__category-item js-filter-item js-gt-filter-item"> <div class="mdc-touch-target-wrapper"> <div class="mdc-form-field"> <div class="mdc-checkbox mdc-checkbox--touch glue-checkbox"> <input type="checkbox" class="mdc-checkbox__native-control" id="filter-research_area-general-science" name="filter-research_area-general-science" value="general-science" > <div class="mdc-checkbox__background"> <svg class="mdc-checkbox__checkmark" viewBox="0 0 24 24"> <path class="mdc-checkbox__checkmark-path" fill="none" d="M1.73,12.91 8.1,19.28 22.79,4.59"></path> </svg> <div class="mdc-checkbox__mixedmark"></div> </div> <div class="mdc-checkbox__ripple"></div> </div> <label class="filter-option-label" for="filter-research_area-general-science"> <span class="js-filter-option-label-text">General Science</span> <span class="filter-option-label-count">1</span></label> </div> </div> </li> <li class="glue-filter__category-item js-filter-item js-gt-filter-item"> <div class="mdc-touch-target-wrapper"> <div class="mdc-form-field"> <div class="mdc-checkbox mdc-checkbox--touch glue-checkbox"> <input type="checkbox" class="mdc-checkbox__native-control" id="filter-research_area-machine-intelligence" name="filter-research_area-machine-intelligence" value="machine-intelligence" > <div class="mdc-checkbox__background"> <svg class="mdc-checkbox__checkmark" viewBox="0 0 24 24"> <path class="mdc-checkbox__checkmark-path" fill="none" d="M1.73,12.91 8.1,19.28 22.79,4.59"></path> </svg> <div class="mdc-checkbox__mixedmark"></div> </div> <div class="mdc-checkbox__ripple"></div> </div> <label class="filter-option-label" for="filter-research_area-machine-intelligence"> <span class="js-filter-option-label-text">Machine Intelligence</span> <span class="filter-option-label-count">5</span></label> </div> </div> </li> <li class="glue-filter__category-item js-filter-item js-gt-filter-item"> <div class="mdc-touch-target-wrapper"> <div class="mdc-form-field"> <div class="mdc-checkbox mdc-checkbox--touch glue-checkbox"> <input type="checkbox" class="mdc-checkbox__native-control" id="filter-research_area-machine-perception" name="filter-research_area-machine-perception" value="machine-perception" > <div class="mdc-checkbox__background"> <svg class="mdc-checkbox__checkmark" viewBox="0 0 24 24"> <path class="mdc-checkbox__checkmark-path" fill="none" d="M1.73,12.91 8.1,19.28 22.79,4.59"></path> </svg> <div class="mdc-checkbox__mixedmark"></div> </div> <div class="mdc-checkbox__ripple"></div> </div> <label class="filter-option-label" for="filter-research_area-machine-perception"> <span class="js-filter-option-label-text">Machine Perception</span> <span class="filter-option-label-count">5</span></label> </div> </div> </li> <li class="glue-filter__category-item js-filter-item js-gt-filter-item"> <div class="mdc-touch-target-wrapper"> <div class="mdc-form-field"> <div class="mdc-checkbox mdc-checkbox--touch glue-checkbox"> <input type="checkbox" class="mdc-checkbox__native-control" id="filter-research_area-responsible-ai" name="filter-research_area-responsible-ai" value="responsible-ai" > <div class="mdc-checkbox__background"> <svg class="mdc-checkbox__checkmark" viewBox="0 0 24 24"> <path class="mdc-checkbox__checkmark-path" fill="none" d="M1.73,12.91 8.1,19.28 22.79,4.59"></path> </svg> <div class="mdc-checkbox__mixedmark"></div> </div> <div class="mdc-checkbox__ripple"></div> </div> <label class="filter-option-label" for="filter-research_area-responsible-ai"> <span class="js-filter-option-label-text">Responsible AI</span> <span class="filter-option-label-count">1</span></label> </div> </div> </li> </ul> </div> </div> </div> <div class="glue-expansion-panel "> <h3 class="glue-expansion-panel__toggle"> <span class="glue-expansion-panel__button" id="filter-toggle-team" data-glue-expansion-panel-toggle-for="filter-panel-team"> <span class="glue-expansion-panel__header-text" id="filter-team-label">Teams</span> <svg role="presentation" aria-hidden="true" class="glue-icon glue-expansion-panel__header-arrow"> <use href="/gr/static/assets/icons/glue-icons.svg#arrow-drop-down"></use> </svg> </span> </h3>  <div class="glue-expansion-panel__content" id="filter-panel-team"> <div> <ul class="glue-filter__category glue-no-bullet" data-glue-filter-category="team" role="list" aria-controls="filter-results" aria-labelledby="filter-team-label"> <li class="glue-filter__category-item js-filter-item js-gt-filter-item"> <div class="mdc-touch-target-wrapper"> <div class="mdc-form-field"> <div class="mdc-checkbox mdc-checkbox--touch glue-checkbox"> <input type="checkbox" class="mdc-checkbox__native-control" id="filter-team-cloud-ai" name="filter-team-cloud-ai" value="cloud-ai" > <div class="mdc-checkbox__background"> <svg class="mdc-checkbox__checkmark" viewBox="0 0 24 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data-gt-id="publications_list" data-gt-component-name=""> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/adamatch-a-unified-approach-to-semi-supervised-learning-and-domain-adaptation/ > AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Becca Roelofs </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> David Berthelot </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Kihyuk Sohn </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/nicholascarlini/"> Nicholas Carlini </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/alexeykurakin/"> Alex Kurakin </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> ICLR (2022) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentwe-extend-semi-supervised-learning tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentwe-extend-semi-supervised-learning" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">We extend semi-supervised learning to the problem of domain adaptation to learn significantly higher-accuracy models that train on one data distribution and test on a different one. With the goal of generality, we introduce AdaMatch, a method that unifies the tasks of unsupervised domain adaptation (UDA), semi-supervised learning (SSL), and semi-supervised domain adaptation (SSDA). In an extensive experimental study, we compare its behavior with respective state-of-the-art techniques from SSL, SSDA, and UDA on vision classification tasks. We find AdaMatch either matches or significantly exceeds the state-of-the-art in each case using the same hyper-parameters regardless of the dataset or task. For example, AdaMatch nearly doubles the accuracy compared to that of the prior state-of-the-art on the UDA task for DomainNet and even exceeds the accuracy of the prior state-of-the-art obtained with pre-training by 6.4% when AdaMatch is trained completely from scratch. Furthermore, by providing AdaMatch with just one labeled example per class from the target domain (i.e., the SSDA setting), we increase the target accuracy by an additional 6.1%, and with 5 labeled examples, by 13.6%.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/adamatch-a-unified-approach-to-semi-supervised-learning-and-domain-adaptation/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/remixmatch-semi-supervised-learning-with-distribution-matching-and-augmentation-anchoring/ > ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/alexeykurakin/"> Alex Kurakin </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Colin Raffel </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> David Berthelot </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/ekindoguscubuk/"> Ekin Dogus Cubuk </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Han Zhang </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Kihyuk Sohn </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/nicholascarlini/"> Nicholas Carlini </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> ICLR, ICLR (2020) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentwe-improve-the-recently-proposed tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentwe-improve-the-recently-proposed" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">We improve the recently-proposed ``MixMatch'' semi-supervised learning algorithm by introducing two new techniques: distribution alignment and augmentation anchoring. Distribution alignment encourages the marginal distribution of predictions on unlabeled data to be close to the marginal distribution of groundtruth labels. Augmentation anchoring feeds multiple strongly augmented versions of an input into the model and encourages each output to be close to the prediction for a weakly-augmented version of the same input. To produce strong augmentations, we propose a variant of AutoAugment which learns the augmentation policy while the model is being trained. Our new algorithm, dubbed ReMixMatch, is significantly more data-efficient than prior work, requiring between 5x and 16x less data to reach the same accuracy. For example, on CIFAR10 with 250 labeled examples we reach 93.73% accuracy (compared to MixMatch’s accuracy of 93.58% with 4,000 examples) and a median accuracy of 84.92% with just four labels per class. We make our code and data open-source at https://github.com/google-research/remixmatch.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/remixmatch-semi-supervised-learning-with-distribution-matching-and-augmentation-anchoring/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/fixmatch-simplifying-semi-supervised-learning-with-consistency-and-confidence/ > FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/alexeykurakin/"> Alex Kurakin </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Chun-Liang Li </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Colin Raffel </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> David Berthelot </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/ekindoguscubuk/"> Ekin Dogus Cubuk </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Han Zhang </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Kihyuk Sohn </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/nicholascarlini/"> Nicholas Carlini </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/106816/"> Zizhao Zhang </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> NeurIPS (2020) (to appear) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentsemi-supervised-learning-ssl-pro tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentsemi-supervised-learning-ssl-pro" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model’s performance. This domain has seen fast progress recently, at the cost of requiring more complex methods. In this paper we proposeFixMatch, an algorithm that is a significant simplification of existing SSL methods.FixMatch first generates pseudo-labels using the model’s predictions on weakly-augmented unlabeled images. For a given image, the pseudo-label is only retained if the model produces a high-confidence prediction. The model is then trained to predict the pseudo-label when fed a strongly-augmented version of the same image. Despite its simplicity, we show that FixMatch achieves state-of-the-art performance across a variety of standard semi-supervised learning benchmarks, including 94.93% accuracy on CIFAR-10 with 250 labels and 88.61% accuracy with 40 – just 4 labels per class. We carry out an extensive ablation study to tease apart the experimental factors that are most important to FixMatch’s success</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/fixmatch-simplifying-semi-supervised-learning-with-consistency-and-confidence/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/ensemble-adversarial-training-attacks-and-defenses/ > Ensemble Adversarial Training: Attacks and Defenses </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/alexeykurakin/"> Alex Kurakin </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Dan Boneh </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Florian Tramèr </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Ian Goodfellow </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/nicolaspapernot/"> Nicolas Papernot </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Patrick McDaniel </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> ICLR (2018) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentadversarial-examples-are-perturbed tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentadversarial-examples-are-perturbed" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Adversarial examples are perturbed inputs designed to fool machine learning models. Adversarial training injects such examples into training data to increase robustness. To scale this technique to large datasets, perturbations are crafted using fast single-step methods that maximize a linear approximation of the model’s loss. We show that this form of adversarial training converges to a degenerate global minimum, wherein small curvature artifacts near the data points obfuscate a linear approximation of the loss. The model thus learns to generate weak perturbations, rather than defend against strong ones. As a result, we find that adversarial training remains vulnerable to black-box attacks, where we transfer perturbations computed on undefended models, as well as to a powerful novel single-step attack that escapes the non-smooth vicinity of the input data via a small random step. We further introduce Ensemble Adversarial Training, a technique that augments training data with perturbations transferred from other models. We use ensemble adversarial training to train ImageNet models with strong robustness to black-box attacks. In particular, our most robust model won the first round of the NIPS 2017 competition on Defenses against Adversarial Attacks</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/ensemble-adversarial-training-attacks-and-defenses/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/adversarial-examples-that-fool-both-computer-vision-and-time-limited-humans/ > Adversarial Examples that Fool both Computer Vision and Time-Limited Humans </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Gamaleldin Fathy Elsayed </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Shreya Shankar </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Brian Cheung </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/nicolaspapernot/"> Nicolas Papernot </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/alexeykurakin/"> Alex Kurakin </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Ian Goodfellow </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Jascha Sohl-dickstein </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> NeurIPS (2018) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentmachine-learning-models-are-vulner tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentmachine-learning-models-are-vulner" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich. However, it is still an open question whether humans are prone to similar mistakes. Here, we address this question by leveraging recent techniques that transfer adversarial examples from computer vision models with known parameters and architecture to other models with unknown parameters and architecture, and by matching the initial processing of the human visual system. We find that adversarial examples that strongly transfer across computer vision models influence the classifications made by time-limited human observers.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/adversarial-examples-that-fool-both-computer-vision-and-time-limited-humans/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/adversarial-examples-in-the-physical-world/ > Adversarial examples in the physical world </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/alexeykurakin/"> Alexey Kurakin </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Ian Goodfellow </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Samy Bengio </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> ICLR Workshop (2017) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentmost-existing-machine-learning-cla tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentmost-existing-machine-learning-cla" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Most existing machine learning classifiers are highly vulnerable to adversarial examples. An adversarial example is a sample of input data which has been modified very slightly in a way that is intended to cause a machine learning classifier to misclassify it. In many cases, these modifications can be so subtle that a human observer does not even notice the modification at all, yet the classifier still makes a mistake. Adversarial examples pose security concerns because they could be used to perform an attack on machine learning systems, even if the adversary has no access to the underlying model. Up to now, all previous work have assumed a threat model in which the adversary can feed data directly into the machine learning classifier. This is not always the case for systems operating in the physical world, for example those which are using signals from cameras and other sensors as an input. This paper shows that even in such physical world scenarios, machine learning systems are vulnerable to adversarial examples. We demonstrate this by feeding adversarial images obtained from cell-phone camera to an ImageNet Inception classifier and measuring the classification accuracy of the system. We find that a large fraction of adversarial examples are classified incorrectly even when perceived through the camera.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/adversarial-examples-in-the-physical-world/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/adversarial-machine-learning-at-scale/ > Adversarial Machine Learning at Scale </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/alexeykurakin/"> Alexey Kurakin </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Ian J. Goodfellow </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Samy Bengio </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> ICLR (2017) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentadversarial-examples-are-malicious tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentadversarial-examples-are-malicious" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Adversarial examples are malicious inputs designed to fool machine learning models. They often transfer from one model to another, allowing attackers to mount black box attacks without knowledge of the target model's parameters. Adversarial training is the process of explicitly training a model on adversarial examples, in order to make it more robust to attack or to reduce its test error on clean inputs. So far, adversarial training has primarily been applied to small problems. In this research, we apply adversarial training to ImageNet. Our contributions include: (1) recommendations for how to succesfully scale adversarial training to large models and datasets, (2) the observation that adversarial training confers robustness to single-step attack methods, (3) the finding that multi-step attack methods are somewhat less transferable than single-step attack methods, so single-step attacks are the best for mounting black-box attacks, and (4) resolution of a "label leaking" effect that causes adversarially trained models to perform better on adversarial examples than on clean examples, because the adversarial example construction process uses the true label and the model can learn to exploit regularities in the construction process.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/adversarial-machine-learning-at-scale/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/large-scale-evolution-of-image-classifiers/ > Large-Scale Evolution of Image Classifiers </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/estebanreal/"> Esteban Real </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/104893/"> Sherry Moore </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Andrew Selle </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/saurabhsaxena/"> Saurabh Saxena </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Yutaka Leon Suematsu </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/quocle/"> Quoc Le </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/alexeykurakin/"> Alex Kurakin </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> ICML (2017) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentneural-networks-have-proven-effect tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentneural-networks-have-proven-effect" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone. Evolutionary algorithms provide a technique to discover such networks automatically. Despite significant computational requirements, we show that evolving models that rival large, hand-designed architectures is possible today. We employ simple evolutionary techniques at unprecedented scales to discover models for the CIFAR-10 and CIFAR-100 datasets, starting from trivial initial conditions. To do this, we use novel and intuitive mutation operators that navigate large search spaces. We stress that no human participation is required once evolution starts and that the output is a fully-trained model. Throughout this work, we place special emphasis on the repeatability of results, the variability in the outcomes and the computational requirements.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/large-scale-evolution-of-image-classifiers/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> </div> <div class="filtered-list-base__pagination" data-hot-swap="pagination"> </div> </div> </div> </section> <section class="google_scholar_search">  <a href="#" class="js-google-scholar-search-link glue-button glue-button--low-emphasis glue-button--icon" title="See more related publications on Google Scholar" target="_blank"> <svg role="presentation" aria-hidden="true" class="glue-icon glue-icon--24px "> <use href="/gr/static/assets/icons/glue-icons.svg#open-in-new"></use> </svg> Search on Google Scholar </a> </section> </div> <section class="banner --theme-" data-gt-id="banner" data-gt-component-name=""> 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