{"list":"other","total":67,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"2105.05233","title":"Diffusion Models Beat GANs on Image Synthesis","created":"2021-05-11","cited_by_count":8255},{"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":"1912.04958","title":"Analyzing and Improving the Image Quality of StyleGAN","created":"2019-12-03","cited_by_count":3588},{"id":"1606.08415","title":"Gaussian Error Linear Units (GELUs)","created":"2016-06-27","cited_by_count":3502},{"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":"1905.04899","title":"CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features","created":"2019-05-13","cited_by_count":2884},{"id":"1903.12261","title":"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations","created":"2019-03-28","cited_by_count":2877},{"id":"1809.11096","title":"Large Scale GAN Training for High Fidelity Natural Image Synthesis","created":"2018-09-28","cited_by_count":2626},{"id":"1909.13719","title":"RandAugment: Practical automated data augmentation with a reduced search space","created":"2019-09-30","cited_by_count":2269},{"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":"1607.02533","title":"Adversarial examples in the physical world","created":"2016-07-08","cited_by_count":1769},{"id":"1708.04552","title":"Improved Regularization of Convolutional Neural Networks with Cutout","created":"2017-08-15","cited_by_count":1628},{"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":"1803.05407","title":"Averaging Weights Leads to Wider Optima and Better Generalization","created":"2018-03-14","cited_by_count":1209},{"id":"1604.07316","title":"End to End Learning for Self-Driving Cars","created":"2016-04-25","cited_by_count":1009},{"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":"1805.09501","title":"AutoAugment: Learning Augmentation Policies from Data","created":"2018-05-24","cited_by_count":730},{"id":"1708.06131","title":"Evasion Attacks against Machine Learning at Test Time","created":"2017-08-21","cited_by_count":689},{"id":"2010.09670","title":"RobustBench: a standardized adversarial robustness benchmark","created":"2020-10-19","cited_by_count":661},{"id":"2004.02546","title":"GANSpace: Discovering Interpretable GAN Controls","created":"2020-04-06","cited_by_count":614},{"id":"2002.08347","title":"On Adaptive Attacks to Adversarial Example Defenses","created":"2020-02-19","cited_by_count":555},{"id":"1705.07263","title":"Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods","created":"2017-05-20","cited_by_count":546},{"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":"1805.06605","title":"Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models","created":"2018-05-17","cited_by_count":362},{"id":"1804.11285","title":"Adversarially Robust Generalization Requires More Data","created":"2018-04-30","cited_by_count":318},{"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":"2011.10650","title":"Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images","created":"2020-11-20","cited_by_count":264},{"id":"1907.07171","title":"On the \"steerability\" of generative adversarial networks","created":"2019-07-16","cited_by_count":248},{"id":"1802.05666","title":"Adversarial Risk and the Dangers of Evaluating Against Weak Attacks","created":"2018-02-15","cited_by_count":221},{"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":"1905.13725","title":"Are Labels Required for Improving Adversarial Robustness?","created":"2019-05-31","cited_by_count":163},{"id":"1905.10291","title":"Privacy Risks of Securing Machine Learning Models against Adversarial Examples","created":"2019-05-24","cited_by_count":141},{"id":"2006.12655","title":"Perceptual Adversarial Robustness: Defense Against Unseen Threat Models","created":"2020-06-22","cited_by_count":137},{"id":"1905.10887","title":"Classification Accuracy Score for Conditional Generative Models","created":"2019-05-26","cited_by_count":136},{"id":"2002.08619","title":"Boosting Adversarial Training with Hypersphere Embedding","created":"2020-02-20","cited_by_count":97},{"id":"2001.10238","title":"Controlling generative models with continuous factors of variations","created":"2020-01-28","cited_by_count":93},{"id":"2011.11164","title":"Learnable Boundary Guided Adversarial Training","created":"2020-11-23","cited_by_count":93},{"id":"2010.01279","title":"Do Wider Neural Networks Really Help Adversarial Robustness?","created":"2020-10-03","cited_by_count":79},{"id":"1906.00555","title":"Adversarially Robust Generalization Just Requires More Unlabeled Data","created":"2019-06-03","cited_by_count":76},{"id":"1905.11971","title":"ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation","created":"2019-05-28","cited_by_count":65},{"id":"2002.04599","title":"Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations","created":"2020-02-11","cited_by_count":55},{"id":"1909.12272","title":"Lower Bounds on Adversarial Robustness from Optimal Transport","created":"2019-09-26","cited_by_count":53},{"id":"1905.13021","title":"Robustness to Adversarial Perturbations in Learning from Incomplete Data","created":"2019-05-24","cited_by_count":49},{"id":"2007.08450","title":"Learning perturbation sets for robust machine learning","created":"2020-07-16","cited_by_count":48},{"id":"1812.09803","title":"Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks","created":"2018-12-24","cited_by_count":46},{"id":"1910.09338","title":"An Alternative Surrogate Loss for PGD-based Adversarial Testing","created":"2019-10-21","cited_by_count":45},{"id":"2006.08669","title":"On Adversarial Bias and the Robustness of Fair Machine Learning","created":"2020-06-15","cited_by_count":36},{"id":"1811.05232","title":"Theoretical Analysis of Adversarial Learning: A Minimax Approach","created":"2018-11-13","cited_by_count":31},{"id":"1912.03192","title":"Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations","created":"2019-12-06","cited_by_count":28},{"id":"1810.12042","title":"Logit Pairing Methods Can Fool Gradient-Based Attacks","created":"2018-10-29","cited_by_count":27},{"id":"1912.02184","title":"Towards Robust Image Classification Using Sequential Attention Models","created":"2019-12-04","cited_by_count":27},{"id":"1909.09756","title":"Scale MLPerf-0.6 models on Google TPU-v3 Pods","created":"2019-09-21","cited_by_count":14}],"next_cursor":null}