{"list":"other","total":118,"rows":[{"id":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"1502.03167","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","created":"2015-02-11","cited_by_count":7005},{"id":"2104.14294","title":"Emerging Properties in Self-Supervised Vision Transformers","created":"2021-04-29","cited_by_count":6968},{"id":"1706.06083","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","created":"2017-06-19","cited_by_count":6415},{"id":"1710.09412","title":"mixup: Beyond Empirical Risk Minimization","created":"2017-10-25","cited_by_count":5183},{"id":"1809.09600","title":"HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering","created":"2018-09-25","cited_by_count":3754},{"id":"1608.04644","title":"Towards Evaluating the Robustness of Neural Networks","created":"2016-08-16","cited_by_count":2998},{"id":"1905.04899","title":"CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features","created":"2019-05-13","cited_by_count":2884},{"id":"1312.6034","title":"Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps","created":"2013-12-20","cited_by_count":2371},{"id":"2004.07780","title":"Shortcut Learning in Deep Neural Networks","created":"2020-04-16","cited_by_count":1969},{"id":"1908.09635","title":"A Survey on Bias and Fairness in Machine Learning","created":"2019-08-23","cited_by_count":1917},{"id":"1907.02893","title":"Invariant Risk Minimization","created":"2019-07-05","cited_by_count":1854},{"id":"2006.16241","title":"The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization","created":"2020-06-29","cited_by_count":1850},{"id":"1708.04552","title":"Improved Regularization of Convolutional Neural Networks with Cutout","created":"2017-08-15","cited_by_count":1628},{"id":"1902.02918","title":"Certified Adversarial Robustness via Randomized Smoothing","created":"2019-02-08","cited_by_count":1340},{"id":"1511.04599","title":"DeepFool: a simple and accurate method to fool deep neural networks","created":"2015-11-14","cited_by_count":1318},{"id":"1604.07316","title":"End to End Learning for Self-Driving Cars","created":"2016-04-25","cited_by_count":1009},{"id":"2005.04118","title":"Beyond Accuracy: Behavioral Testing of NLP models with CheckList","created":"2020-05-08","cited_by_count":1006},{"id":"1905.02175","title":"Adversarial Examples Are Not Bugs, They Are Features","created":"2019-05-06","cited_by_count":953},{"id":"1611.01236","title":"Adversarial Machine Learning at Scale","created":"2016-11-04","cited_by_count":936},{"id":"1912.02781","title":"AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty","created":"2019-12-05","cited_by_count":901},{"id":"1705.07204","title":"Ensemble Adversarial Training: Attacks and Defenses","created":"2017-05-19","cited_by_count":840},{"id":"2110.11334","title":"Generalized Out-of-Distribution Detection: A Survey","created":"2021-10-21","cited_by_count":802},{"id":"1511.07528","title":"The Limitations of Deep Learning in Adversarial Settings","created":"2015-11-24","cited_by_count":790},{"id":"1511.04508","title":"Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks","created":"2015-11-14","cited_by_count":678},{"id":"1907.11932","title":"Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment","created":"2019-07-27","cited_by_count":671},{"id":"1708.03999","title":"ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models","created":"2017-08-14","cited_by_count":666},{"id":"1904.12843","title":"Adversarial Training for Free!","created":"2019-04-29","cited_by_count":643},{"id":"1607.08221","title":"MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition","created":"2016-07-27","cited_by_count":624},{"id":"1703.00848","title":"Unsupervised Image-to-Image Translation Networks","created":"2017-03-02","cited_by_count":603},{"id":"2009.00236","title":"A Survey of Deep Active Learning","created":"2020-08-30","cited_by_count":554},{"id":"2011.03395","title":"Underspecification Presents Challenges for Credibility in Modern Machine Learning","created":"2020-11-06","cited_by_count":496},{"id":"2007.00644","title":"Measuring Robustness to Natural Distribution Shifts in Image Classification","created":"2020-07-01","cited_by_count":454},{"id":"1605.07277","title":"Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples","created":"2016-05-24","cited_by_count":446},{"id":"1908.05355","title":"The generalization error of random features regression: Precise asymptotics and double descent curve","created":"2019-08-14","cited_by_count":413},{"id":"1606.05908","title":"Tutorial on Variational Autoencoders","created":"2016-06-19","cited_by_count":409},{"id":"1901.09960","title":"Using Pre-Training Can Improve Model Robustness and Uncertainty","created":"2019-01-28","cited_by_count":373},{"id":"1705.06640","title":"DeepXplore: Automated Whitebox Testing of Deep Learning Systems","created":"2017-05-18","cited_by_count":311},{"id":"2009.06962","title":"Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup","created":"2020-09-15","cited_by_count":261},{"id":"1801.02610","title":"Generating Adversarial Examples with Adversarial Networks","created":"2018-01-08","cited_by_count":258},{"id":"1708.08559","title":"DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars","created":"2017-08-28","cited_by_count":251},{"id":"2002.11242","title":"Attacks Which Do Not Kill Training Make Adversarial Learning Stronger","created":"2020-02-26","cited_by_count":243},{"id":"1908.07086","title":"Human uncertainty makes classification more robust","created":"2019-08-19","cited_by_count":235},{"id":"2110.01167","title":"Trustworthy AI: From Principles to Practices","created":"2021-10-04","cited_by_count":207},{"id":"2010.01736","title":"Geometry-aware Instance-reweighted Adversarial Training","created":"2020-10-05","cited_by_count":198},{"id":"1802.01396","title":"To understand deep learning we need to understand kernel learning","created":"2018-02-05","cited_by_count":187},{"id":"2106.07411","title":"Partial success in closing the gap between human and machine vision","created":"2021-06-14","cited_by_count":185},{"id":"1811.09716","title":"Robustness via curvature regularization, and vice versa","created":"2018-11-23","cited_by_count":175},{"id":"1805.10820","title":"Local Rule-Based Explanations of Black Box Decision Systems","created":"2018-05-28","cited_by_count":166},{"id":"1712.00673","title":"Towards Robust Neural Networks via Random Self-ensemble","created":"2017-12-02","cited_by_count":159},{"id":"1803.07519","title":"DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems","created":"2018-03-20","cited_by_count":147},{"id":"1912.11078","title":"Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview","created":"2019-11-09","cited_by_count":137},{"id":"2103.01050","title":"Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World","created":"2021-03-01","cited_by_count":127},{"id":"1808.08444","title":"Guiding Deep Learning System Testing using Surprise Adequacy","created":"2018-08-25","cited_by_count":124},{"id":"1909.11825","title":"Unsupervised Domain Adaptation through Self-Supervision","created":"2019-09-26","cited_by_count":116},{"id":"2107.11277","title":"Machine Learning with a Reject Option: A survey","created":"2021-07-23","cited_by_count":109},{"id":"2203.03818","title":"Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon","created":"2022-03-08","cited_by_count":101},{"id":"1906.07927","title":"SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing","created":"2019-06-19","cited_by_count":93},{"id":"2104.06728","title":"Adversarial Sticker: A Stealthy Attack Method in the Physical World","created":"2021-04-14","cited_by_count":93},{"id":"2107.06641","title":"Trustworthy AI: A Computational Perspective","created":"2021-07-12","cited_by_count":93},{"id":"1805.11090","title":"GenAttack: Practical Black-box Attacks with Gradient-Free Optimization","created":"2018-05-28","cited_by_count":82},{"id":"1712.02779","title":"Exploring the Landscape of Spatial Robustness","created":"2017-12-07","cited_by_count":81},{"id":"2108.07969","title":"Revisiting Adversarial Robustness Distillation: Robust Soft Labels Make Student Better","created":"2021-08-18","cited_by_count":79},{"id":"1909.00900","title":"Metric Learning for Adversarial Robustness","created":"2019-09-03","cited_by_count":77},{"id":"2103.06504","title":"Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink","created":"2021-03-11","cited_by_count":75},{"id":"2007.00753","title":"Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey","created":"2020-07-01","cited_by_count":72},{"id":"1807.10875","title":"TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing","created":"2018-07-28","cited_by_count":68},{"id":"2305.10862","title":"How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses","created":"2023-05-18","cited_by_count":66},{"id":"1906.02168","title":"Do Image Classifiers Generalize Across Time?","created":"2019-06-05","cited_by_count":59},{"id":"1807.06732","title":"Motivating the Rules of the Game for Adversarial Example Research","created":"2018-07-18","cited_by_count":57},{"id":"1911.00484","title":"Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents","created":"2019-11-01","cited_by_count":57},{"id":"2108.04204","title":"Meta Gradient Adversarial Attack","created":"2021-08-09","cited_by_count":55},{"id":"2305.10435","title":"Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions","created":"2023-05-11","cited_by_count":55},{"id":"1710.07859","title":"Feature-Guided Black-Box Safety Testing of Deep Neural Networks","created":"2017-10-21","cited_by_count":49},{"id":"1905.06635","title":"Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization","created":"2019-05-16","cited_by_count":49},{"id":"1812.02257","title":"On Testing Machine Learning Programs","created":"2018-12-05","cited_by_count":48},{"id":"2112.00639","title":"A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?","created":"2021-12-01","cited_by_count":48},{"id":"2204.09274","title":"Causality-based Neural Network Repair","created":"2022-04-20","cited_by_count":47},{"id":"2111.04578","title":"Improved Regularization and Robustness for Fine-tuning in Neural Networks","created":"2021-11-08","cited_by_count":45},{"id":"2009.05487","title":"The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples","created":"2020-09-11","cited_by_count":44},{"id":"2103.04623","title":"Consistency Regularization for Adversarial Robustness","created":"2021-03-08","cited_by_count":42},{"id":"2104.04413","title":"Provable Repair of Deep Neural Networks","created":"2021-04-09","cited_by_count":42},{"id":"2206.12227","title":"Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective","created":"2022-06-24","cited_by_count":42},{"id":"1912.10185","title":"Jacobian Adversarially Regularized Networks for Robustness","created":"2019-12-21","cited_by_count":40},{"id":"2011.03083","title":"A Tunable Robust Pruning Framework Through Dynamic Network Rewiring of DNNs","created":"2020-11-03","cited_by_count":37},{"id":"2204.11790","title":"Can Rationalization Improve Robustness?","created":"2022-04-25","cited_by_count":36},{"id":"1608.06048","title":"Survey of resampling techniques for improving classification performance in unbalanced datasets","created":"2016-08-22","cited_by_count":33},{"id":"2104.02466","title":"A Review of Formal Methods applied to Machine Learning","created":"2021-04-06","cited_by_count":33},{"id":"1902.05974","title":"DeepFault: Fault Localization for Deep Neural Networks","created":"2019-02-15","cited_by_count":30},{"id":"1907.11484","title":"Multi-level Domain Adaptive learning for Cross-Domain Detection","created":"2019-07-26","cited_by_count":30},{"id":"2010.01278","title":"Efficient Robust Training via Backward Smoothing","created":"2020-10-03","cited_by_count":29},{"id":"2109.11690","title":"Discovering and Validating AI Errors With Crowdsourced Failure Reports","created":"2021-09-23","cited_by_count":28},{"id":"1912.12463","title":"Arachne: Search Based Repair of Deep Neural Networks","created":"2019-12-28","cited_by_count":26},{"id":"1812.03705","title":"Defending Against Universal Perturbations With Shared Adversarial Training","created":"2018-12-10","cited_by_count":25},{"id":"1909.02436","title":"Are Adversarial Robustness and Common Perturbation Robustness Independent Attributes ?","created":"2019-09-04","cited_by_count":25},{"id":"1705.05264","title":"Extending Defensive Distillation","created":"2017-05-15","cited_by_count":23},{"id":"2307.07653","title":"RFLA: A Stealthy Reflected Light Adversarial Attack in the Physical World","created":"2023-07-14","cited_by_count":23},{"id":"1702.06763","title":"DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples","created":"2017-02-22","cited_by_count":22},{"id":"1611.08309","title":"On Human Intellect and Machine Failures: Troubleshooting Integrative Machine Learning Systems","created":"2016-11-24","cited_by_count":18}],"next_cursor":"100"}