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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/2410.18308">arXiv:2410.18308</a> <span> [<a href="https://arxiv.org/pdf/2410.18308">pdf</a>, <a href="https://arxiv.org/format/2410.18308">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Operating Systems">cs.OS</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/10.1145/3696355.3699702">10.1145/3696355.3699702 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Exact schedulability test for sporadic mixed-criticality real-time systems using antichains and oracles </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Picard%2C+S">Simon Picard</a>, <a href="/search/cs?searchtype=author&query=Paolillo%2C+A">Antonio Paolillo</a>, <a href="/search/cs?searchtype=author&query=Geeraerts%2C+G">Gilles Geeraerts</a>, <a href="/search/cs?searchtype=author&query=Goossens%2C+J">Jo毛l Goossens</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="2410.18308v1-abstract-short" style="display: inline;"> This work addresses the problem of exact schedulability assessment in uniprocessor mixed-criticality real-time systems with sporadic task sets. We model the problem by means of a finite automaton that has to be explored in order to check for schedulability. To mitigate the state explosion problem, we provide a generic algorithm which is parameterised by several techniques called oracles and simula… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.18308v1-abstract-full').style.display = 'inline'; document.getElementById('2410.18308v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.18308v1-abstract-full" style="display: none;"> This work addresses the problem of exact schedulability assessment in uniprocessor mixed-criticality real-time systems with sporadic task sets. We model the problem by means of a finite automaton that has to be explored in order to check for schedulability. To mitigate the state explosion problem, we provide a generic algorithm which is parameterised by several techniques called oracles and simulation relations. These techniques leverage results from the scheduling literature as "plug-ins" that make the algorithm more efficient in practice. Our approach achieves up to a 99.998% reduction in the search space required for exact schedulability testing, making it practical for a range of task sets, up to 8 tasks or maximum periods of 350. This method enables to challenge the pessimism of an existing schedulability test and to derive a new dynamic-priority scheduler, demonstrating its good performance. This is the full version of an RTNS 2024 paper. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.18308v1-abstract-full').style.display = 'none'; document.getElementById('2410.18308v1-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> 23 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </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">In the proceedings of 32nd International Conference on Real-Time Networks and Systems, RTNS24, ACM, 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2103.10529">arXiv:2103.10529</a> <span> [<a href="https://arxiv.org/pdf/2103.10529">pdf</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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> White Paper Machine Learning in Certified Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Delseny%2C+H">Herv茅 Delseny</a>, <a href="/search/cs?searchtype=author&query=Gabreau%2C+C">Christophe Gabreau</a>, <a href="/search/cs?searchtype=author&query=Gauffriau%2C+A">Adrien Gauffriau</a>, <a href="/search/cs?searchtype=author&query=Beaudouin%2C+B">Bernard Beaudouin</a>, <a href="/search/cs?searchtype=author&query=Ponsolle%2C+L">Ludovic Ponsolle</a>, <a href="/search/cs?searchtype=author&query=Alecu%2C+L">Lucian Alecu</a>, <a href="/search/cs?searchtype=author&query=Bonnin%2C+H">Hugues Bonnin</a>, <a href="/search/cs?searchtype=author&query=Beltran%2C+B">Brice Beltran</a>, <a href="/search/cs?searchtype=author&query=Duchel%2C+D">Didier Duchel</a>, <a href="/search/cs?searchtype=author&query=Ginestet%2C+J">Jean-Brice Ginestet</a>, <a href="/search/cs?searchtype=author&query=Hervieu%2C+A">Alexandre Hervieu</a>, <a href="/search/cs?searchtype=author&query=Martinez%2C+G">Ghilaine Martinez</a>, <a href="/search/cs?searchtype=author&query=Pasquet%2C+S">Sylvain Pasquet</a>, <a href="/search/cs?searchtype=author&query=Delmas%2C+K">Kevin Delmas</a>, <a href="/search/cs?searchtype=author&query=Pagetti%2C+C">Claire Pagetti</a>, <a href="/search/cs?searchtype=author&query=Gabriel%2C+J">Jean-Marc Gabriel</a>, <a href="/search/cs?searchtype=author&query=Chapdelaine%2C+C">Camille Chapdelaine</a>, <a href="/search/cs?searchtype=author&query=Picard%2C+S">Sylvaine Picard</a>, <a href="/search/cs?searchtype=author&query=Damour%2C+M">Mathieu Damour</a>, <a href="/search/cs?searchtype=author&query=Cappi%2C+C">Cyril Cappi</a>, <a href="/search/cs?searchtype=author&query=Gard%C3%A8s%2C+L">Laurent Gard猫s</a>, <a href="/search/cs?searchtype=author&query=De+Grancey%2C+F">Florence De Grancey</a>, <a href="/search/cs?searchtype=author&query=Jenn%2C+E">Eric Jenn</a>, <a href="/search/cs?searchtype=author&query=Lefevre%2C+B">Baptiste Lefevre</a>, <a href="/search/cs?searchtype=author&query=Flandin%2C+G">Gregory Flandin</a> , et al. (3 additional authors not shown) </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="2103.10529v1-abstract-short" style="display: inline;"> Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving vehicles, recognizing voice, etc. It is also an opportunity to implement and embed new capabilities out of the reach of classical implementation techniques. However, ML techniques introduce new potential risks. Therefore, t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.10529v1-abstract-full').style.display = 'inline'; document.getElementById('2103.10529v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2103.10529v1-abstract-full" style="display: none;"> Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving vehicles, recognizing voice, etc. It is also an opportunity to implement and embed new capabilities out of the reach of classical implementation techniques. However, ML techniques introduce new potential risks. Therefore, they have only been applied in systems where their benefits are considered worth the increase of risk. In practice, ML techniques raise multiple challenges that could prevent their use in systems submitted to certification constraints. But what are the actual challenges? Can they be overcome by selecting appropriate ML techniques, or by adopting new engineering or certification practices? These are some of the questions addressed by the ML Certification 3 Workgroup (WG) set-up by the Institut de Recherche Technologique Saint Exup茅ry de Toulouse (IRT), as part of the DEEL Project. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.10529v1-abstract-full').style.display = 'none'; document.getElementById('2103.10529v1-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> 18 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2021. </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">113 pages, White paper</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> S079L03T00-005 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2; K.7.3 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2101.03020">arXiv:2101.03020</a> <span> [<a href="https://arxiv.org/pdf/2101.03020">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Databases">cs.DB</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"> Dataset Definition Standard (DDS) </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cappi%2C+C">Cyril Cappi</a>, <a href="/search/cs?searchtype=author&query=Chapdelaine%2C+C">Camille Chapdelaine</a>, <a href="/search/cs?searchtype=author&query=Gardes%2C+L">Laurent Gardes</a>, <a href="/search/cs?searchtype=author&query=Jenn%2C+E">Eric Jenn</a>, <a href="/search/cs?searchtype=author&query=Lefevre%2C+B">Baptiste Lefevre</a>, <a href="/search/cs?searchtype=author&query=Picard%2C+S">Sylvaine Picard</a>, <a href="/search/cs?searchtype=author&query=Soumarmon%2C+T">Thomas Soumarmon</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="2101.03020v1-abstract-short" style="display: inline;"> This document gives a set of recommendations to build and manipulate the datasets used to develop and/or validate machine learning models such as deep neural networks. This document is one of the 3 documents defined in [1] to ensure the quality of datasets. This is a work in progress as good practices evolve along with our understanding of machine learning. The document is divided into three main… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.03020v1-abstract-full').style.display = 'inline'; document.getElementById('2101.03020v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2101.03020v1-abstract-full" style="display: none;"> This document gives a set of recommendations to build and manipulate the datasets used to develop and/or validate machine learning models such as deep neural networks. This document is one of the 3 documents defined in [1] to ensure the quality of datasets. This is a work in progress as good practices evolve along with our understanding of machine learning. The document is divided into three main parts. Section 2 addresses the data collection activity. Section 3 gives recommendations about the annotation process. Finally, Section 4 gives recommendations concerning the breakdown between train, validation, and test datasets. In each part, we first define the desired properties at stake, then we explain the objectives targeted to meet the properties, finally we state the recommendations to reach these objectives. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.03020v1-abstract-full').style.display = 'none'; document.getElementById('2101.03020v1-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> 7 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2101.01480">arXiv:2101.01480</a> <span> [<a href="https://arxiv.org/pdf/2101.01480">pdf</a>, <a href="https://arxiv.org/format/2101.01480">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 Propagation for Few-Shot Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lifchitz%2C+Y">Yann Lifchitz</a>, <a href="/search/cs?searchtype=author&query=Avrithis%2C+Y">Yannis Avrithis</a>, <a href="/search/cs?searchtype=author&query=Picard%2C+S">Sylvaine Picard</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="2101.01480v1-abstract-short" style="display: inline;"> The challenge in few-shot learning is that available data is not enough to capture the underlying distribution. To mitigate this, two emerging directions are (a) using local image representations, essentially multiplying the amount of data by a constant factor, and (b) using more unlabeled data, for instance by transductive inference, jointly on a number of queries. In this work, we bring these tw… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.01480v1-abstract-full').style.display = 'inline'; document.getElementById('2101.01480v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2101.01480v1-abstract-full" style="display: none;"> The challenge in few-shot learning is that available data is not enough to capture the underlying distribution. To mitigate this, two emerging directions are (a) using local image representations, essentially multiplying the amount of data by a constant factor, and (b) using more unlabeled data, for instance by transductive inference, jointly on a number of queries. In this work, we bring these two ideas together, introducing \emph{local propagation}. We treat local image features as independent examples, we build a graph on them and we use it to propagate both the features themselves and the labels, known and unknown. Interestingly, since there is a number of features per image, even a single query gives rise to transductive inference. As a result, we provide a universally safe choice for few-shot inference under both non-transductive and transductive settings, improving accuracy over corresponding methods. This is in contrast to existing solutions, where one needs to choose the method depending on the quantity of available data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.01480v1-abstract-full').style.display = 'none'; document.getElementById('2101.01480v1-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> 5 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2021. </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">ICPR 2020</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2011.01799">arXiv:2011.01799</a> <span> [<a href="https://arxiv.org/pdf/2011.01799">pdf</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="Computation">stat.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Ensuring Dataset Quality for Machine Learning Certification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Picard%2C+S">Sylvaine Picard</a>, <a href="/search/cs?searchtype=author&query=Chapdelaine%2C+C">Camille Chapdelaine</a>, <a href="/search/cs?searchtype=author&query=Cappi%2C+C">Cyril Cappi</a>, <a href="/search/cs?searchtype=author&query=Gardes%2C+L">Laurent Gardes</a>, <a href="/search/cs?searchtype=author&query=Jenn%2C+E">Eric Jenn</a>, <a href="/search/cs?searchtype=author&query=Lef%C3%A8vre%2C+B">Baptiste Lef猫vre</a>, <a href="/search/cs?searchtype=author&query=Soumarmon%2C+T">Thomas Soumarmon</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="2011.01799v1-abstract-short" style="display: inline;"> In this paper, we address the problem of dataset quality in the context of Machine Learning (ML)-based critical systems. We briefly analyse the applicability of some existing standards dealing with data and show that the specificities of the ML context are neither properly captured nor taken into ac-count. As a first answer to this concerning situation, we propose a dataset specification and verif… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.01799v1-abstract-full').style.display = 'inline'; document.getElementById('2011.01799v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2011.01799v1-abstract-full" style="display: none;"> In this paper, we address the problem of dataset quality in the context of Machine Learning (ML)-based critical systems. We briefly analyse the applicability of some existing standards dealing with data and show that the specificities of the ML context are neither properly captured nor taken into ac-count. As a first answer to this concerning situation, we propose a dataset specification and verification process, and apply it on a signal recognition system from the railway domain. In addi-tion, we also give a list of recommendations for the collection and management of datasets. This work is one step towards the dataset engineering process that will be required for ML to be used on safety critical systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.01799v1-abstract-full').style.display = 'none'; document.getElementById('2011.01799v1-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> 3 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> The 10th IEEE International Workshop on Software Certification (WoSoCer 2020) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2002.07522">arXiv:2002.07522</a> <span> [<a href="https://arxiv.org/pdf/2002.07522">pdf</a>, <a href="https://arxiv.org/format/2002.07522">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"> Few-Shot Few-Shot Learning and the role of Spatial Attention </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lifchitz%2C+Y">Yann Lifchitz</a>, <a href="/search/cs?searchtype=author&query=Avrithis%2C+Y">Yannis Avrithis</a>, <a href="/search/cs?searchtype=author&query=Picard%2C+S">Sylvaine Picard</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="2002.07522v1-abstract-short" style="display: inline;"> Few-shot learning is often motivated by the ability of humans to learn new tasks from few examples. However, standard few-shot classification benchmarks assume that the representation is learned on a limited amount of base class data, ignoring the amount of prior knowledge that a human may have accumulated before learning new tasks. At the same time, even if a powerful representation is available,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.07522v1-abstract-full').style.display = 'inline'; document.getElementById('2002.07522v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.07522v1-abstract-full" style="display: none;"> Few-shot learning is often motivated by the ability of humans to learn new tasks from few examples. However, standard few-shot classification benchmarks assume that the representation is learned on a limited amount of base class data, ignoring the amount of prior knowledge that a human may have accumulated before learning new tasks. At the same time, even if a powerful representation is available, it may happen in some domain that base class data are limited or non-existent. This motivates us to study a problem where the representation is obtained from a classifier pre-trained on a large-scale dataset of a different domain, assuming no access to its training process, while the base class data are limited to few examples per class and their role is to adapt the representation to the domain at hand rather than learn from scratch. We adapt the representation in two stages, namely on the few base class data if available and on the even fewer data of new tasks. In doing so, we obtain from the pre-trained classifier a spatial attention map that allows focusing on objects and suppressing background clutter. This is important in the new problem, because when base class data are few, the network cannot learn where to focus implicitly. We also show that a pre-trained network may be easily adapted to novel classes, without meta-learning. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.07522v1-abstract-full').style.display = 'none'; document.getElementById('2002.07522v1-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> 18 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1908.07253">arXiv:1908.07253</a> <span> [<a href="https://arxiv.org/pdf/1908.07253">pdf</a>, <a href="https://arxiv.org/format/1908.07253">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">stat.ML</span> <span class="tag is-small is-grey 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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> n-MeRCI: A new Metric to Evaluate the Correlation Between Predictive Uncertainty and True Error </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Moukari%2C+M">Michel Moukari</a>, <a href="/search/cs?searchtype=author&query=Simon%2C+L">Lo茂c Simon</a>, <a href="/search/cs?searchtype=author&query=Picard%2C+S">Sylvaine Picard</a>, <a href="/search/cs?searchtype=author&query=Jurie%2C+F">Fr茅d茅ric Jurie</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="1908.07253v1-abstract-short" style="display: inline;"> As deep learning applications are becoming more and more pervasive in robotics, the question of evaluating the reliability of inferences becomes a central question in the robotics community. This domain, known as predictive uncertainty, has come under the scrutiny of research groups developing Bayesian approaches adapted to deep learning such as Monte Carlo Dropout. Unfortunately, for the time bei… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1908.07253v1-abstract-full').style.display = 'inline'; document.getElementById('1908.07253v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1908.07253v1-abstract-full" style="display: none;"> As deep learning applications are becoming more and more pervasive in robotics, the question of evaluating the reliability of inferences becomes a central question in the robotics community. This domain, known as predictive uncertainty, has come under the scrutiny of research groups developing Bayesian approaches adapted to deep learning such as Monte Carlo Dropout. Unfortunately, for the time being, the real goal of predictive uncertainty has been swept under the rug. Indeed, these approaches are solely evaluated in terms of raw performance of the network prediction, while the quality of their estimated uncertainty is not assessed. Evaluating such uncertainty prediction quality is especially important in robotics, as actions shall depend on the confidence in perceived information. In this context, the main contribution of this article is to propose a novel metric that is adapted to the evaluation of relative uncertainty assessment and directly applicable to regression with deep neural networks. To experimentally validate this metric, we evaluate it on a toy dataset and then apply it to the task of monocular depth estimation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1908.07253v1-abstract-full').style.display = 'none'; document.getElementById('1908.07253v1-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> 20 August, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IEEE/RJS International Conference on Intelligent Robots and Systems (IROS), In press </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1903.05050">arXiv:1903.05050</a> <span> [<a href="https://arxiv.org/pdf/1903.05050">pdf</a>, <a href="https://arxiv.org/format/1903.05050">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"> Dense Classification and Implanting for Few-Shot Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lifchitz%2C+Y">Yann Lifchitz</a>, <a href="/search/cs?searchtype=author&query=Avrithis%2C+Y">Yannis Avrithis</a>, <a href="/search/cs?searchtype=author&query=Picard%2C+S">Sylvaine Picard</a>, <a href="/search/cs?searchtype=author&query=Bursuc%2C+A">Andrei Bursuc</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="1903.05050v1-abstract-short" style="display: inline;"> Training deep neural networks from few examples is a highly challenging and key problem for many computer vision tasks. In this context, we are targeting knowledge transfer from a set with abundant data to other sets with few available examples. We propose two simple and effective solutions: (i) dense classification over feature maps, which for the first time studies local activations in the domai… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1903.05050v1-abstract-full').style.display = 'inline'; document.getElementById('1903.05050v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1903.05050v1-abstract-full" style="display: none;"> Training deep neural networks from few examples is a highly challenging and key problem for many computer vision tasks. In this context, we are targeting knowledge transfer from a set with abundant data to other sets with few available examples. We propose two simple and effective solutions: (i) dense classification over feature maps, which for the first time studies local activations in the domain of few-shot learning, and (ii) implanting, that is, attaching new neurons to a previously trained network to learn new, task-specific features. On miniImageNet, we improve the prior state-of-the-art on few-shot classification, i.e., we achieve 62.5%, 79.8% and 83.8% on 5-way 1-shot, 5-shot and 10-shot settings respectively. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1903.05050v1-abstract-full').style.display = 'none'; document.getElementById('1903.05050v1-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 March, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2019. </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">CVPR 2019</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1811.01749">arXiv:1811.01749</a> <span> [<a href="https://arxiv.org/pdf/1811.01749">pdf</a>, <a href="https://arxiv.org/format/1811.01749">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="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> FUNN: Flexible Unsupervised Neural Network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Vigouroux%2C+D">David Vigouroux</a>, <a href="/search/cs?searchtype=author&query=Picard%2C+S">Sylvain Picard</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="1811.01749v1-abstract-short" style="display: inline;"> Deep neural networks have demonstrated high accuracy in image classification tasks. However, they were shown to be weak against adversarial examples: a small perturbation in the image which changes the classification output dramatically. In recent years, several defenses have been proposed to solve this issue in supervised classification tasks. We propose a method to obtain robust features in unsu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.01749v1-abstract-full').style.display = 'inline'; document.getElementById('1811.01749v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1811.01749v1-abstract-full" style="display: none;"> Deep neural networks have demonstrated high accuracy in image classification tasks. However, they were shown to be weak against adversarial examples: a small perturbation in the image which changes the classification output dramatically. In recent years, several defenses have been proposed to solve this issue in supervised classification tasks. We propose a method to obtain robust features in unsupervised learning tasks against adversarial attacks. Our method differs from existing solutions by directly learning the robust features without the need to project the adversarial examples in the original examples distribution space. A first auto-encoder A1 is in charge of perturbing the input image to fool another auto-encoder A2 which is in charge of regenerating the original image. A1 tries to find the less perturbed image under the constraint that the error in the output of A2 should be at least equal to a threshold. Thanks to this training, the encoder of A2 will be robust against adversarial attacks and could be used in different tasks like classification. Using state-of-art network architectures, we demonstrate the robustness of the features obtained thanks to this method in classification tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.01749v1-abstract-full').style.display = 'none'; document.getElementById('1811.01749v1-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> 5 November, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1806.03051">arXiv:1806.03051</a> <span> [<a href="https://arxiv.org/pdf/1806.03051">pdf</a>, <a href="https://arxiv.org/format/1806.03051">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"> Deep multi-scale architectures for monocular depth estimation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Moukari%2C+M">Michel Moukari</a>, <a href="/search/cs?searchtype=author&query=Picard%2C+S">Sylvaine Picard</a>, <a href="/search/cs?searchtype=author&query=Simon%2C+L">Loic Simon</a>, <a href="/search/cs?searchtype=author&query=Jurie%2C+F">Fr茅d茅ric Jurie</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="1806.03051v1-abstract-short" style="display: inline;"> This paper aims at understanding the role of multi-scale information in the estimation of depth from monocular images. More precisely, the paper investigates four different deep CNN architectures, designed to explicitly make use of multi-scale features along the network, and compare them to a state-of-the-art single-scale approach. The paper also shows that involving multi-scale features in depth… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1806.03051v1-abstract-full').style.display = 'inline'; document.getElementById('1806.03051v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1806.03051v1-abstract-full" style="display: none;"> This paper aims at understanding the role of multi-scale information in the estimation of depth from monocular images. More precisely, the paper investigates four different deep CNN architectures, designed to explicitly make use of multi-scale features along the network, and compare them to a state-of-the-art single-scale approach. The paper also shows that involving multi-scale features in depth estimation not only improves the performance in terms of accuracy, but also gives qualitatively better depth maps. Experiments are done on the widely used NYU Depth dataset, on which the proposed method achieves state-of-the-art performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1806.03051v1-abstract-full').style.display = 'none'; document.getElementById('1806.03051v1-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> 8 June, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2018. </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 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a 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