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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Colin, J"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.03993">arXiv:2411.03993</a> <span> [<a href="https://arxiv.org/pdf/2411.03993">pdf</a>, <a href="https://arxiv.org/format/2411.03993">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Local vs distributed representations: What is the right basis for interpretability? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Colin%2C+J">Julien Colin</a>, <a href="/search/cs?searchtype=author&query=Goetschalckx%2C+L">Lore Goetschalckx</a>, <a href="/search/cs?searchtype=author&query=Fel%2C+T">Thomas Fel</a>, <a href="/search/cs?searchtype=author&query=Boutin%2C+V">Victor Boutin</a>, <a href="/search/cs?searchtype=author&query=Gopal%2C+J">Jay Gopal</a>, <a href="/search/cs?searchtype=author&query=Serre%2C+T">Thomas Serre</a>, <a href="/search/cs?searchtype=author&query=Oliver%2C+N">Nuria Oliver</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.03993v1-abstract-short" style="display: inline;"> Much of the research on the interpretability of deep neural networks has focused on studying the visual features that maximally activate individual neurons. However, recent work has cast doubts on the usefulness of such local representations for understanding the behavior of deep neural networks because individual neurons tend to respond to multiple unrelated visual patterns, a phenomenon referred… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03993v1-abstract-full').style.display = 'inline'; document.getElementById('2411.03993v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.03993v1-abstract-full" style="display: none;"> Much of the research on the interpretability of deep neural networks has focused on studying the visual features that maximally activate individual neurons. However, recent work has cast doubts on the usefulness of such local representations for understanding the behavior of deep neural networks because individual neurons tend to respond to multiple unrelated visual patterns, a phenomenon referred to as "superposition". A promising alternative to disentangle these complex patterns is learning sparsely distributed vector representations from entire network layers, as the resulting basis vectors seemingly encode single identifiable visual patterns consistently. Thus, one would expect the resulting code to align better with human perceivable visual patterns, but supporting evidence remains, at best, anecdotal. To fill this gap, we conducted three large-scale psychophysics experiments collected from a pool of 560 participants. Our findings provide (i) strong evidence that features obtained from sparse distributed representations are easier to interpret by human observers and (ii) that this effect is more pronounced in the deepest layers of a neural network. Complementary analyses also reveal that (iii) features derived from sparse distributed representations contribute more to the model's decision. Overall, our results highlight that distributed representations constitute a superior basis for interpretability, underscoring a need for the field to move beyond the interpretation of local neural codes in favor of sparsely distributed ones. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03993v1-abstract-full').style.display = 'none'; document.getElementById('2411.03993v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.06805">arXiv:2306.06805</a> <span> [<a href="https://arxiv.org/pdf/2306.06805">pdf</a>, <a href="https://arxiv.org/format/2306.06805">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Unlocking Feature Visualization for Deeper Networks with MAgnitude Constrained Optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Fel%2C+T">Thomas Fel</a>, <a href="/search/cs?searchtype=author&query=Boissin%2C+T">Thibaut Boissin</a>, <a href="/search/cs?searchtype=author&query=Boutin%2C+V">Victor Boutin</a>, <a href="/search/cs?searchtype=author&query=Picard%2C+A">Agustin Picard</a>, <a href="/search/cs?searchtype=author&query=Novello%2C+P">Paul Novello</a>, <a href="/search/cs?searchtype=author&query=Colin%2C+J">Julien Colin</a>, <a href="/search/cs?searchtype=author&query=Linsley%2C+D">Drew Linsley</a>, <a href="/search/cs?searchtype=author&query=Rousseau%2C+T">Tom Rousseau</a>, <a href="/search/cs?searchtype=author&query=Cad%C3%A8ne%2C+R">R茅mi Cad猫ne</a>, <a href="/search/cs?searchtype=author&query=Goetschalckx%2C+L">Lore Goetschalckx</a>, <a href="/search/cs?searchtype=author&query=Gardes%2C+L">Laurent Gardes</a>, <a href="/search/cs?searchtype=author&query=Serre%2C+T">Thomas Serre</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.06805v3-abstract-short" style="display: inline;"> Feature visualization has gained substantial popularity, particularly after the influential work by Olah et al. in 2017, which established it as a crucial tool for explainability. However, its widespread adoption has been limited due to a reliance on tricks to generate interpretable images, and corresponding challenges in scaling it to deeper neural networks. Here, we describe MACO, a simple appro… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.06805v3-abstract-full').style.display = 'inline'; document.getElementById('2306.06805v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.06805v3-abstract-full" style="display: none;"> Feature visualization has gained substantial popularity, particularly after the influential work by Olah et al. in 2017, which established it as a crucial tool for explainability. However, its widespread adoption has been limited due to a reliance on tricks to generate interpretable images, and corresponding challenges in scaling it to deeper neural networks. Here, we describe MACO, a simple approach to address these shortcomings. The main idea is to generate images by optimizing the phase spectrum while keeping the magnitude constant to ensure that generated explanations lie in the space of natural images. Our approach yields significantly better results (both qualitatively and quantitatively) and unlocks efficient and interpretable feature visualizations for large state-of-the-art neural networks. We also show that our approach exhibits an attribution mechanism allowing us to augment feature visualizations with spatial importance. We validate our method on a novel benchmark for comparing feature visualization methods, and release its visualizations for all classes of the ImageNet dataset on https://serre-lab.github.io/Lens/. Overall, our approach unlocks, for the first time, feature visualizations for large, state-of-the-art deep neural networks without resorting to any parametric prior image model. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.06805v3-abstract-full').style.display = 'none'; document.getElementById('2306.06805v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Conference on Neural Information Processing Systems (NeurIPS), 2023 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2301.11722">arXiv:2301.11722</a> <span> [<a href="https://arxiv.org/pdf/2301.11722">pdf</a>, <a href="https://arxiv.org/format/2301.11722">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Boutin%2C+V">Victor Boutin</a>, <a href="/search/cs?searchtype=author&query=Fel%2C+T">Thomas Fel</a>, <a href="/search/cs?searchtype=author&query=Singhal%2C+L">Lakshya Singhal</a>, <a href="/search/cs?searchtype=author&query=Mukherji%2C+R">Rishav Mukherji</a>, <a href="/search/cs?searchtype=author&query=Nagaraj%2C+A">Akash Nagaraj</a>, <a href="/search/cs?searchtype=author&query=Colin%2C+J">Julien Colin</a>, <a href="/search/cs?searchtype=author&query=Serre%2C+T">Thomas Serre</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2301.11722v3-abstract-short" style="display: inline;"> An important milestone for AI is the development of algorithms that can produce drawings that are indistinguishable from those of humans. Here, we adapt the 'diversity vs. recognizability' scoring framework from Boutin et al, 2022 and find that one-shot diffusion models have indeed started to close the gap between humans and machines. However, using a finer-grained measure of the originality of in… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.11722v3-abstract-full').style.display = 'inline'; document.getElementById('2301.11722v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.11722v3-abstract-full" style="display: none;"> An important milestone for AI is the development of algorithms that can produce drawings that are indistinguishable from those of humans. Here, we adapt the 'diversity vs. recognizability' scoring framework from Boutin et al, 2022 and find that one-shot diffusion models have indeed started to close the gap between humans and machines. However, using a finer-grained measure of the originality of individual samples, we show that strengthening the guidance of diffusion models helps improve the humanness of their drawings, but they still fall short of approximating the originality and recognizability of human drawings. Comparing human category diagnostic features, collected through an online psychophysics experiment, against those derived from diffusion models reveals that humans rely on fewer and more localized features. Overall, our study suggests that diffusion models have significantly helped improve the quality of machine-generated drawings; however, a gap between humans and machines remains -- in part explainable by discrepancies in visual strategies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.11722v3-abstract-full').style.display = 'none'; document.getElementById('2301.11722v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.10154">arXiv:2211.10154</a> <span> [<a href="https://arxiv.org/pdf/2211.10154">pdf</a>, <a href="https://arxiv.org/format/2211.10154">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> CRAFT: Concept Recursive Activation FacTorization for Explainability </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Fel%2C+T">Thomas Fel</a>, <a href="/search/cs?searchtype=author&query=Picard%2C+A">Agustin Picard</a>, <a href="/search/cs?searchtype=author&query=Bethune%2C+L">Louis Bethune</a>, <a href="/search/cs?searchtype=author&query=Boissin%2C+T">Thibaut Boissin</a>, <a href="/search/cs?searchtype=author&query=Vigouroux%2C+D">David Vigouroux</a>, <a href="/search/cs?searchtype=author&query=Colin%2C+J">Julien Colin</a>, <a href="/search/cs?searchtype=author&query=Cad%C3%A8ne%2C+R">R茅mi Cad猫ne</a>, <a href="/search/cs?searchtype=author&query=Serre%2C+T">Thomas Serre</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2211.10154v2-abstract-short" style="display: inline;"> Attribution methods, which employ heatmaps to identify the most influential regions of an image that impact model decisions, have gained widespread popularity as a type of explainability method. However, recent research has exposed the limited practical value of these methods, attributed in part to their narrow focus on the most prominent regions of an image -- revealing "where" the model looks, b… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.10154v2-abstract-full').style.display = 'inline'; document.getElementById('2211.10154v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.10154v2-abstract-full" style="display: none;"> Attribution methods, which employ heatmaps to identify the most influential regions of an image that impact model decisions, have gained widespread popularity as a type of explainability method. However, recent research has exposed the limited practical value of these methods, attributed in part to their narrow focus on the most prominent regions of an image -- revealing "where" the model looks, but failing to elucidate "what" the model sees in those areas. In this work, we try to fill in this gap with CRAFT -- a novel approach to identify both "what" and "where" by generating concept-based explanations. We introduce 3 new ingredients to the automatic concept extraction literature: (i) a recursive strategy to detect and decompose concepts across layers, (ii) a novel method for a more faithful estimation of concept importance using Sobol indices, and (iii) the use of implicit differentiation to unlock Concept Attribution Maps. We conduct both human and computer vision experiments to demonstrate the benefits of the proposed approach. We show that the proposed concept importance estimation technique is more faithful to the model than previous methods. When evaluating the usefulness of the method for human experimenters on a human-centered utility benchmark, we find that our approach significantly improves on two of the three test scenarios. Our code is freely available at github.com/deel-ai/Craft. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.10154v2-abstract-full').style.display = 'none'; document.getElementById('2211.10154v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Proceedings of the IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2206.05379">arXiv:2206.05379</a> <span> [<a href="https://arxiv.org/pdf/2206.05379">pdf</a>, <a href="https://arxiv.org/format/2206.05379">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> A Benchmark for Compositional Visual Reasoning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zerroug%2C+A">Aimen Zerroug</a>, <a href="/search/cs?searchtype=author&query=Vaishnav%2C+M">Mohit Vaishnav</a>, <a href="/search/cs?searchtype=author&query=Colin%2C+J">Julien Colin</a>, <a href="/search/cs?searchtype=author&query=Musslick%2C+S">Sebastian Musslick</a>, <a href="/search/cs?searchtype=author&query=Serre%2C+T">Thomas Serre</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2206.05379v1-abstract-short" style="display: inline;"> A fundamental component of human vision is our ability to parse complex visual scenes and judge the relations between their constituent objects. AI benchmarks for visual reasoning have driven rapid progress in recent years with state-of-the-art systems now reaching human accuracy on some of these benchmarks. Yet, a major gap remains in terms of the sample efficiency with which humans and AI system… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.05379v1-abstract-full').style.display = 'inline'; document.getElementById('2206.05379v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.05379v1-abstract-full" style="display: none;"> A fundamental component of human vision is our ability to parse complex visual scenes and judge the relations between their constituent objects. AI benchmarks for visual reasoning have driven rapid progress in recent years with state-of-the-art systems now reaching human accuracy on some of these benchmarks. Yet, a major gap remains in terms of the sample efficiency with which humans and AI systems learn new visual reasoning tasks. Humans' remarkable efficiency at learning has been at least partially attributed to their ability to harness compositionality -- such that they can efficiently take advantage of previously gained knowledge when learning new tasks. Here, we introduce a novel visual reasoning benchmark, Compositional Visual Relations (CVR), to drive progress towards the development of more data-efficient learning algorithms. We take inspiration from fluidic intelligence and non-verbal reasoning tests and describe a novel method for creating compositions of abstract rules and associated image datasets at scale. Our proposed benchmark includes measures of sample efficiency, generalization and transfer across task rules, as well as the ability to leverage compositionality. We systematically evaluate modern neural architectures and find that, surprisingly, convolutional architectures surpass transformer-based architectures across all performance measures in most data regimes. However, all computational models are a lot less data efficient compared to humans even after learning informative visual representations using self-supervision. Overall, we hope that our challenge will spur interest in the development of neural architectures that can learn to harness compositionality toward more efficient learning. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.05379v1-abstract-full').style.display = 'none'; document.getElementById('2206.05379v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2206.04394">arXiv:2206.04394</a> <span> [<a href="https://arxiv.org/pdf/2206.04394">pdf</a>, <a href="https://arxiv.org/format/2206.04394">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Xplique: A Deep Learning Explainability Toolbox </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Fel%2C+T">Thomas Fel</a>, <a href="/search/cs?searchtype=author&query=Hervier%2C+L">Lucas Hervier</a>, <a href="/search/cs?searchtype=author&query=Vigouroux%2C+D">David Vigouroux</a>, <a href="/search/cs?searchtype=author&query=Poche%2C+A">Antonin Poche</a>, <a href="/search/cs?searchtype=author&query=Plakoo%2C+J">Justin Plakoo</a>, <a href="/search/cs?searchtype=author&query=Cadene%2C+R">Remi Cadene</a>, <a href="/search/cs?searchtype=author&query=Chalvidal%2C+M">Mathieu Chalvidal</a>, <a href="/search/cs?searchtype=author&query=Colin%2C+J">Julien Colin</a>, <a href="/search/cs?searchtype=author&query=Boissin%2C+T">Thibaut Boissin</a>, <a href="/search/cs?searchtype=author&query=Bethune%2C+L">Louis Bethune</a>, <a href="/search/cs?searchtype=author&query=Picard%2C+A">Agustin Picard</a>, <a href="/search/cs?searchtype=author&query=Nicodeme%2C+C">Claire Nicodeme</a>, <a href="/search/cs?searchtype=author&query=Gardes%2C+L">Laurent Gardes</a>, <a href="/search/cs?searchtype=author&query=Flandin%2C+G">Gregory Flandin</a>, <a href="/search/cs?searchtype=author&query=Serre%2C+T">Thomas Serre</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2206.04394v1-abstract-short" style="display: inline;"> Today's most advanced machine-learning models are hardly scrutable. The key challenge for explainability methods is to help assisting researchers in opening up these black boxes, by revealing the strategy that led to a given decision, by characterizing their internal states or by studying the underlying data representation. To address this challenge, we have developed Xplique: a software library f… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.04394v1-abstract-full').style.display = 'inline'; document.getElementById('2206.04394v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.04394v1-abstract-full" style="display: none;"> Today's most advanced machine-learning models are hardly scrutable. The key challenge for explainability methods is to help assisting researchers in opening up these black boxes, by revealing the strategy that led to a given decision, by characterizing their internal states or by studying the underlying data representation. To address this challenge, we have developed Xplique: a software library for explainability which includes representative explainability methods as well as associated evaluation metrics. It interfaces with one of the most popular learning libraries: Tensorflow as well as other libraries including PyTorch, scikit-learn and Theano. The code is licensed under the MIT license and is freely available at github.com/deel-ai/xplique. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.04394v1-abstract-full').style.display = 'none'; document.getElementById('2206.04394v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.04417">arXiv:2112.04417</a> <span> [<a href="https://arxiv.org/pdf/2112.04417">pdf</a>, <a href="https://arxiv.org/format/2112.04417">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Colin%2C+J">Julien Colin</a>, <a href="/search/cs?searchtype=author&query=Fel%2C+T">Thomas Fel</a>, <a href="/search/cs?searchtype=author&query=Cadene%2C+R">Remi Cadene</a>, <a href="/search/cs?searchtype=author&query=Serre%2C+T">Thomas Serre</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2112.04417v3-abstract-short" style="display: inline;"> A multitude of explainability methods and associated fidelity performance metrics have been proposed to help better understand how modern AI systems make decisions. However, much of the current work has remained theoretical -- without much consideration for the human end-user. In particular, it is not yet known (1) how useful current explainability methods are in practice for more real-world scena… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.04417v3-abstract-full').style.display = 'inline'; document.getElementById('2112.04417v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.04417v3-abstract-full" style="display: none;"> A multitude of explainability methods and associated fidelity performance metrics have been proposed to help better understand how modern AI systems make decisions. However, much of the current work has remained theoretical -- without much consideration for the human end-user. In particular, it is not yet known (1) how useful current explainability methods are in practice for more real-world scenarios and (2) how well associated performance metrics accurately predict how much knowledge individual explanations contribute to a human end-user trying to understand the inner-workings of the system. To fill this gap, we conducted psychophysics experiments at scale to evaluate the ability of human participants to leverage representative attribution methods for understanding the behavior of different image classifiers representing three real-world scenarios: identifying bias in an AI system, characterizing the visual strategy it uses for tasks that are too difficult for an untrained non-expert human observer as well as understanding its failure cases. Our results demonstrate that the degree to which individual attribution methods help human participants better understand an AI system varied widely across these scenarios. This suggests a critical need for the field to move past quantitative improvements of current attribution methods towards the development of complementary approaches that provide qualitatively different sources of information to human end-users. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.04417v3-abstract-full').style.display = 'none'; document.getElementById('2112.04417v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2006.15344">arXiv:2006.15344</a> <span> [<a href="https://arxiv.org/pdf/2006.15344">pdf</a>, <a href="https://arxiv.org/format/2006.15344">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.3390/electronics9101684">10.3390/electronics9101684 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Utilising Deep Learning Techniques for Effective Zero-Day Attack Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hindy%2C+H">Hanan Hindy</a>, <a href="/search/cs?searchtype=author&query=Atkinson%2C+R">Robert Atkinson</a>, <a href="/search/cs?searchtype=author&query=Tachtatzis%2C+C">Christos Tachtatzis</a>, <a href="/search/cs?searchtype=author&query=Colin%2C+J">Jean-No毛l Colin</a>, <a href="/search/cs?searchtype=author&query=Bayne%2C+E">Ethan Bayne</a>, <a href="/search/cs?searchtype=author&query=Bellekens%2C+X">Xavier Bellekens</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2006.15344v2-abstract-short" style="display: inline;"> Machine Learning (ML) and Deep Learning (DL) have been used for building Intrusion Detection Systems (IDS). The increase in both the number and sheer variety of new cyber-attacks poses a tremendous challenge for IDS solutions that rely on a database of historical attack signatures. Therefore, the industrial pull for robust IDSs that are capable of flagging zero-day attacks is growing. Current outl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.15344v2-abstract-full').style.display = 'inline'; document.getElementById('2006.15344v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.15344v2-abstract-full" style="display: none;"> Machine Learning (ML) and Deep Learning (DL) have been used for building Intrusion Detection Systems (IDS). The increase in both the number and sheer variety of new cyber-attacks poses a tremendous challenge for IDS solutions that rely on a database of historical attack signatures. Therefore, the industrial pull for robust IDSs that are capable of flagging zero-day attacks is growing. Current outlier-based zero-day detection research suffers from high false-negative rates, thus limiting their practical use and performance. This paper proposes an autoencoder implementation for detecting zero-day attacks. The aim is to build an IDS model with high recall while keeping the miss rate (false-negatives) to an acceptable minimum. Two well-known IDS datasets are used for evaluation-CICIDS2017 and NSL-KDD. In order to demonstrate the efficacy of our model, we compare its results against a One-Class Support Vector Machine (SVM). The manuscript highlights the performance of a One-Class SVM when zero-day attacks are distinctive from normal behaviour. The proposed model benefits greatly from autoencoders encoding-decoding capabilities. The results show that autoencoders are well-suited at detecting complex zero-day attacks. The results demonstrate a zero-day detection accuracy of [89-99%] for the NSL-KDD dataset and [75-98%] for the CICIDS2017 dataset. Finally, the paper outlines the observed trade-off between recall and fallout. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.15344v2-abstract-full').style.display = 'none'; document.getElementById('2006.15344v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">18 pages, 4 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Electronics 2020, 9, 1684 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1904.07024">arXiv:1904.07024</a> <span> [<a href="https://arxiv.org/pdf/1904.07024">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Discrete Mathematics">cs.DM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Management of mobile resources in Physical Internet logistic models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Colin%2C+J">Jean-Yves Colin</a>, <a href="/search/cs?searchtype=author&query=Nakechbandi%2C+M">Moustafa Nakechbandi</a>, <a href="/search/cs?searchtype=author&query=Mathieu%2C+H">Herv茅 Mathieu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1904.07024v1-abstract-short" style="display: inline;"> This paper deals with the concept of a 'Physical Internet', the idea of building large logistics systems like the very successful Digital Internet network. The idea is to handle mobile resources, such as containers, just like Internet data packets. Thus, it is possible to use the principles of encapsulation and routing to optimize the freight. The problem is that mobile resources, such as containe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.07024v1-abstract-full').style.display = 'inline'; document.getElementById('1904.07024v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1904.07024v1-abstract-full" style="display: none;"> This paper deals with the concept of a 'Physical Internet', the idea of building large logistics systems like the very successful Digital Internet network. The idea is to handle mobile resources, such as containers, just like Internet data packets. Thus, it is possible to use the principles of encapsulation and routing to optimize the freight. The problem is that mobile resources, such as containers, are not quite similar to data packets, because they are real and not dematerialized. Thus the handling and the storing of mobile resources, such as containers, will create imbalances in the logistics network, leading to starvation or overstocking of logistic network nodes. We propose in this paper a study addressing this problem leading to some solutions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.07024v1-abstract-full').style.display = 'none'; document.getElementById('1904.07024v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 April, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> ICALT 2015, 4th IEEE Int. Conf. on Advanced Logistics & Transport, Valenciennes, May 2015, Valenciennes, France </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1904.05069">arXiv:1904.05069</a> <span> [<a href="https://arxiv.org/pdf/1904.05069">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> A Proposal for an Open Logistics Interconnection Reference Model for a Physical Internet </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Colin%2C+J">Jean-Yves Colin</a>, <a href="/search/cs?searchtype=author&query=Mathieu%2C+H">Herv茅 Mathieu</a>, <a href="/search/cs?searchtype=author&query=Nakechbandi%2C+M">Moustafa Nakechbandi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1904.05069v1-abstract-short" style="display: inline;"> This paper presents a New Open Logistics Interconnection (NOLI) reference model for a Physical Internet, inspired by the Open Systems Interconnection (OSI) reference model for data networks. This NOLI model is compared to the OSI model, and to the Transmission Control Protocol/Internet Protocol (TCP/IP) model of Internet. It is also compared to the OLI model for a Physical Internet proposed by Mon… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.05069v1-abstract-full').style.display = 'inline'; document.getElementById('1904.05069v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1904.05069v1-abstract-full" style="display: none;"> This paper presents a New Open Logistics Interconnection (NOLI) reference model for a Physical Internet, inspired by the Open Systems Interconnection (OSI) reference model for data networks. This NOLI model is compared to the OSI model, and to the Transmission Control Protocol/Internet Protocol (TCP/IP) model of Internet. It is also compared to the OLI model for a Physical Internet proposed by Montreuil. The main differences between the presented NOLI model and all the other models named above are in the appearance of definitions of physical objects in different layers and not just the lowest one. Also, the NOLI model we present locates the containerization and de-containerization operations in the topmost layer, and not in the layer below as does the OLI model. Finally, the NOLI model is closer to the TCP/IP and OSI models than the OLI model, keeping the integrity of the Link Layer that the OLI model divides in two layers, and keeping separate the Session and Transport OSI Layers that the OLI model unites in just one layer. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.05069v1-abstract-full').style.display = 'none'; document.getElementById('1904.05069v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 April, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> GOL 2016, The 3rd International IEEE Conference on Logistics Operations Management, May 2016, FEZ, Morocco </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1904.05066">arXiv:1904.05066</a> <span> [<a href="https://arxiv.org/pdf/1904.05066">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Data Structures and Algorithms">cs.DS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Minimum Spanning Trees in Weakly Dynamic Graphs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Nakechbandi%2C+M">Moustafa Nakechbandi</a>, <a href="/search/cs?searchtype=author&query=Colin%2C+J">Jean-Yves Colin</a>, <a href="/search/cs?searchtype=author&query=Mathieu%2C+H">Herv茅 Mathieu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1904.05066v1-abstract-short" style="display: inline;"> In this paper, we study weakly dynamic undirected graphs, that can be used to represent some logistic networks. The goal is to deliver all the delivery points in the network. The network exists in a mostly stable environment, except for a few edges known to be non-stable. The weight of each of these non-stable edges may change at any time (bascule or lift bridge, elevator, traffic congestion...).… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.05066v1-abstract-full').style.display = 'inline'; document.getElementById('1904.05066v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1904.05066v1-abstract-full" style="display: none;"> In this paper, we study weakly dynamic undirected graphs, that can be used to represent some logistic networks. The goal is to deliver all the delivery points in the network. The network exists in a mostly stable environment, except for a few edges known to be non-stable. The weight of each of these non-stable edges may change at any time (bascule or lift bridge, elevator, traffic congestion...). All other edges have stable weights that never change. This problem can be now considered as a Minimum Spanning Tree (MST) problem on a dynamic graph. We propose an efficient polynomial algorithm that computes in advance alternative MSTs for all possible configurations. No additional computation is then needed after any change in the problem because the MSTs are already known in all cases. We use these results to compute critical values for the non-stable weights and to pre-compute best paths. When the non-stable weights change, the appropriate MST may then directly and immediately be used without any recomputation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.05066v1-abstract-full').style.display = 'none'; document.getElementById('1904.05066v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 April, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> LOGISTIQUA 2017, 10th International Colloquium of Logistics and Supply Chain Management, Apr 2017, Rabat, Morocco </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 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