{"list":"other","total":84,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1503.02531","title":"Distilling the Knowledge in a Neural Network","created":"2015-03-09","cited_by_count":10324},{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"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":"1312.6199","title":"Intriguing properties of neural networks","created":"2013-12-21","cited_by_count":5155},{"id":"1608.03983","title":"SGDR: Stochastic Gradient Descent with Warm Restarts","created":"2016-08-13","cited_by_count":5045},{"id":"1605.07146","title":"Wide Residual Networks","created":"2016-05-23","cited_by_count":3070},{"id":"1608.04644","title":"Towards Evaluating the Robustness of Neural Networks","created":"2016-08-16","cited_by_count":2998},{"id":"1909.13719","title":"RandAugment: Practical automated data augmentation with a reduced search space","created":"2019-09-30","cited_by_count":2269},{"id":"1607.02533","title":"Adversarial examples in the physical world","created":"2016-07-08","cited_by_count":1769},{"id":"1611.03530","title":"Understanding deep learning requires rethinking generalization","created":"2016-11-10","cited_by_count":1674},{"id":"1412.6550","title":"FitNets: Hints for Thin Deep Nets","created":"2014-12-19","cited_by_count":1653},{"id":"1901.08573","title":"Theoretically Principled Trade-off between Robustness and Accuracy","created":"2019-01-24","cited_by_count":1517},{"id":"2003.01690","title":"Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks","created":"2020-03-03","cited_by_count":1451},{"id":"1802.00420","title":"Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples","created":"2018-02-01","cited_by_count":1268},{"id":"1612.03928","title":"Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer","created":"2016-12-12","cited_by_count":1077},{"id":"1905.02175","title":"Adversarial Examples Are Not Bugs, They Are Features","created":"2019-05-06","cited_by_count":953},{"id":"1602.02697","title":"Practical Black-Box Attacks against Machine Learning","created":"2016-02-08","cited_by_count":903},{"id":"1805.12152","title":"Robustness May Be at Odds with Accuracy","created":"2018-05-30","cited_by_count":880},{"id":"1705.07204","title":"Ensemble Adversarial Training: Attacks and Defenses","created":"2017-05-19","cited_by_count":840},{"id":"1910.10699","title":"Contrastive Representation Distillation","created":"2019-10-23","cited_by_count":748},{"id":"2001.03994","title":"Fast is better than free: Revisiting adversarial training","created":"2020-01-12","cited_by_count":727},{"id":"1708.06131","title":"Evasion Attacks against Machine Learning at Test Time","created":"2017-08-21","cited_by_count":689},{"id":"1912.00049","title":"Square Attack: a query-efficient black-box adversarial attack via random search","created":"2019-11-29","cited_by_count":685},{"id":"1511.04508","title":"Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks","created":"2015-11-14","cited_by_count":678},{"id":"1904.12843","title":"Adversarial Training for Free!","created":"2019-04-29","cited_by_count":643},{"id":"1711.00117","title":"Countering Adversarial Images using Input Transformations","created":"2017-10-31","cited_by_count":520},{"id":"1712.09665","title":"Adversarial Patch","created":"2017-12-27","cited_by_count":517},{"id":"1907.09682","title":"Similarity-Preserving Knowledge Distillation","created":"2019-07-23","cited_by_count":516},{"id":"2002.11569","title":"Overfitting in adversarially robust deep learning","created":"2020-02-26","cited_by_count":506},{"id":"1905.13736","title":"Unlabeled Data Improves Adversarial Robustness","created":"2019-05-31","cited_by_count":416},{"id":"1901.09960","title":"Using Pre-Training Can Improve Model Robustness and Uncertainty","created":"2019-01-28","cited_by_count":373},{"id":"1904.01866","title":"A Comprehensive Overhaul of Feature Distillation","created":"2019-04-03","cited_by_count":348},{"id":"1907.02044","title":"Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack","created":"2019-07-03","cited_by_count":334},{"id":"1712.02976","title":"Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser","created":"2017-12-08","cited_by_count":329},{"id":"1804.11285","title":"Adversarially Robust Generalization Requires More Data","created":"2018-04-30","cited_by_count":318},{"id":"2007.08489","title":"Do Adversarially Robust ImageNet Models Transfer Better?","created":"2020-07-16","cited_by_count":316},{"id":"1812.03411","title":"Feature Denoising for Improving Adversarial Robustness","created":"2018-12-09","cited_by_count":314},{"id":"2010.03593","title":"Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples","created":"2020-10-07","cited_by_count":278},{"id":"2009.05041","title":"Understanding the Role of Individual Units in a Deep Neural Network","created":"2020-09-10","cited_by_count":255},{"id":"2002.11242","title":"Attacks Which Do Not Kill Training Make Adversarial Learning Stronger","created":"2020-02-26","cited_by_count":243},{"id":"1811.03233","title":"Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons","created":"2018-11-08","cited_by_count":238},{"id":"1711.09404","title":"Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients","created":"2017-11-26","cited_by_count":236},{"id":"2007.02617","title":"Understanding and Improving Fast Adversarial Training","created":"2020-07-06","cited_by_count":214},{"id":"2010.00467","title":"Bag of Tricks for Adversarial Training","created":"2020-10-01","cited_by_count":200},{"id":"2010.01736","title":"Geometry-aware Instance-reweighted Adversarial Training","created":"2020-10-05","cited_by_count":198},{"id":"2010.04819","title":"How Does Mixup Help With Robustness and Generalization?","created":"2020-10-09","cited_by_count":193},{"id":"1905.00877","title":"You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle","created":"2019-05-02","cited_by_count":183},{"id":"1905.13725","title":"Are Labels Required for Improving Adversarial Robustness?","created":"2019-05-31","cited_by_count":163},{"id":"1907.10456","title":"Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems","created":"2019-07-24","cited_by_count":153},{"id":"1412.5068","title":"Towards Deep Neural Network Architectures Robust to Adversarial Examples","created":"2014-12-11","cited_by_count":146},{"id":"1704.00805","title":"On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning","created":"2017-04-03","cited_by_count":142},{"id":"2006.07589","title":"Adversarial Self-Supervised Contrastive Learning","created":"2020-06-13","cited_by_count":139},{"id":"1905.09747","title":"Adversarially Robust Distillation","created":"2019-05-23","cited_by_count":125},{"id":"1903.12561","title":"Adversarial Robustness vs Model Compression, or Both?","created":"2019-03-29","cited_by_count":98},{"id":"2002.08619","title":"Boosting Adversarial Training with Hypersphere Embedding","created":"2020-02-20","cited_by_count":97},{"id":"2006.14536","title":"Smooth Adversarial Training","created":"2020-06-25","cited_by_count":89},{"id":"1909.00900","title":"Metric Learning for Adversarial Robustness","created":"2019-09-03","cited_by_count":77},{"id":"1803.00443","title":"Knowledge Transfer with Jacobian Matching","created":"2018-03-01","cited_by_count":76},{"id":"1906.00555","title":"Adversarially Robust Generalization Just Requires More Unlabeled Data","created":"2019-06-03","cited_by_count":76},{"id":"2103.08307","title":"Improving Adversarial Robustness via Channel-wise Activation Suppressing","created":"2021-03-11","cited_by_count":76},{"id":"1905.09797","title":"Interpreting Adversarially Trained Convolutional Neural Networks","created":"2019-05-23","cited_by_count":75},{"id":"2003.02484","title":"Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization","created":"2020-03-05","cited_by_count":73},{"id":"1905.08232","title":"Adversarially robust transfer learning","created":"2019-05-20","cited_by_count":67},{"id":"1909.11515","title":"Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks","created":"2019-09-25","cited_by_count":54},{"id":"1812.02425","title":"MEAL: Multi-Model Ensemble via Adversarial Learning","created":"2018-12-06","cited_by_count":52},{"id":"1905.13021","title":"Robustness to Adversarial Perturbations in Learning from Incomplete Data","created":"2019-05-24","cited_by_count":49},{"id":"2009.02470","title":"Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks","created":"2020-09-05","cited_by_count":45},{"id":"1901.00532","title":"Adversarial Robustness May Be at Odds With Simplicity","created":"2019-01-02","cited_by_count":39},{"id":"1912.05699","title":"What it Thinks is Important is Important: Robustness Transfers through Input Gradients","created":"2019-12-11","cited_by_count":34},{"id":"2104.05641","title":"Generalization bounds via distillation","created":"2021-04-12","cited_by_count":34},{"id":"1906.03787","title":"Intriguing properties of adversarial training at scale","created":"2019-06-10","cited_by_count":33},{"id":"1905.05186","title":"Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models","created":"2019-05-13","cited_by_count":26},{"id":"2108.09093","title":"Towards Understanding the Generative Capability of Adversarially Robust Classifiers","created":"2021-08-20","cited_by_count":26},{"id":"2109.00946","title":"Adversarial Robustness for Unsupervised Domain Adaptation","created":"2021-09-02","cited_by_count":24},{"id":"1807.06714","title":"Defend Deep Neural Networks Against Adversarial Examples via Fixed and Dynamic Quantized Activation Functions","created":"2018-07-18","cited_by_count":17},{"id":"1904.02057","title":"Interpreting Adversarial Examples by Activation Promotion and Suppression","created":"2019-04-03","cited_by_count":17},{"id":"1906.06875","title":"MixUp as Directional Adversarial Training","created":"2019-06-17","cited_by_count":14},{"id":"1809.08516","title":"Adversarial Defense via Data Dependent Activation Function and Total Variation Minimization","created":"2018-09-23","cited_by_count":9},{"id":"2006.11007","title":"Towards an Adversarially Robust Normalization Approach","created":"2020-06-19","cited_by_count":9},{"id":"1802.05193","title":"Security Analysis and Enhancement of Model Compressed Deep Learning Systems under Adversarial Attacks","created":"2018-02-14","cited_by_count":8},{"id":"2102.07327","title":"Guided Interpolation for Adversarial Training","created":"2021-02-15","cited_by_count":8},{"id":"2103.11589","title":"Adversarially Optimized Mixup for Robust Classification","created":"2021-03-22","cited_by_count":7}],"next_cursor":null}