{"list":"other","total":77,"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":"2111.06377","title":"Masked Autoencoders Are Scalable Vision Learners","created":"2021-11-11","cited_by_count":7403},{"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":"2201.03545","title":"A ConvNet for the 2020s","created":"2022-01-10","cited_by_count":3579},{"id":"1608.04644","title":"Towards Evaluating the Robustness of Neural Networks","created":"2016-08-16","cited_by_count":2998},{"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":"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":"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":"1611.01236","title":"Adversarial Machine Learning at Scale","created":"2016-11-04","cited_by_count":936},{"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":"1912.11370","title":"Big Transfer (BiT): General Visual Representation Learning","created":"2019-12-24","cited_by_count":769},{"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":"2010.09670","title":"RobustBench: a standardized adversarial robustness benchmark","created":"2020-10-19","cited_by_count":661},{"id":"1904.12843","title":"Adversarial Training for Free!","created":"2019-04-29","cited_by_count":643},{"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":"1804.08598","title":"Black-box Adversarial Attacks with Limited Queries and Information","created":"2018-04-23","cited_by_count":501},{"id":"2106.09681","title":"XCiT: Cross-Covariance Image Transformers","created":"2021-06-17","cited_by_count":338},{"id":"1907.02044","title":"Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack","created":"2019-07-03","cited_by_count":334},{"id":"1906.04584","title":"Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers","created":"2019-06-09","cited_by_count":330},{"id":"1702.02284","title":"Adversarial Attacks on Neural Network Policies","created":"2017-02-08","cited_by_count":320},{"id":"1804.11285","title":"Adversarially Robust Generalization Requires More Data","created":"2018-04-30","cited_by_count":318},{"id":"1906.04893","title":"Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks","created":"2019-06-12","cited_by_count":293},{"id":"2102.01356","title":"Recent Advances in Adversarial Training for Adversarial Robustness","created":"2021-02-02","cited_by_count":279},{"id":"2010.03593","title":"Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples","created":"2020-10-07","cited_by_count":278},{"id":"2110.09468","title":"Improving Robustness using Generated Data","created":"2021-10-18","cited_by_count":234},{"id":"1702.04267","title":"On Detecting Adversarial Perturbations","created":"2017-02-14","cited_by_count":232},{"id":"1901.08846","title":"Improving Adversarial Robustness via Promoting Ensemble Diversity","created":"2019-01-25","cited_by_count":213},{"id":"2103.01946","title":"Fixing Data Augmentation to Improve Adversarial Robustness","created":"2021-03-02","cited_by_count":204},{"id":"1705.08475","title":"Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation","created":"2017-05-23","cited_by_count":185},{"id":"1809.03113","title":"Certified Adversarial Robustness with Additive Noise","created":"2018-09-10","cited_by_count":180},{"id":"1811.09716","title":"Robustness via curvature regularization, and vice versa","created":"2018-11-23","cited_by_count":175},{"id":"1712.00673","title":"Towards Robust Neural Networks via Random Self-ensemble","created":"2017-12-02","cited_by_count":159},{"id":"2003.12862","title":"Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning","created":"2020-03-28","cited_by_count":153},{"id":"2002.10716","title":"Understanding and Mitigating the Tradeoff Between Robustness and Accuracy","created":"2020-02-25","cited_by_count":149},{"id":"2206.10550","title":"(Certified!!) Adversarial Robustness for Free!","created":"2022-06-21","cited_by_count":132},{"id":"2104.09425","title":"Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?","created":"2021-04-19","cited_by_count":113},{"id":"2202.10103","title":"Robustness and Accuracy Could Be Reconcilable by (Proper) Definition","created":"2022-02-21","cited_by_count":111},{"id":"2003.01219","title":"Exactly Computing the Local Lipschitz Constant of ReLU Networks","created":"2020-03-02","cited_by_count":102},{"id":"1907.10764","title":"Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training","created":"2019-07-24","cited_by_count":95},{"id":"2203.06616","title":"LAS-AT: Adversarial Training with Learnable Attack Strategy","created":"2022-03-13","cited_by_count":95},{"id":"2110.12976","title":"Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks","created":"2021-10-25","cited_by_count":91},{"id":"2010.04925","title":"Regularizing Neural Networks via Adversarial Model Perturbation","created":"2020-10-10","cited_by_count":78},{"id":"2111.01124","title":"When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?","created":"2021-11-01","cited_by_count":77},{"id":"2202.13711","title":"Evaluating the Adversarial Robustness of Adaptive Test-time Defenses","created":"2022-02-28","cited_by_count":64},{"id":"2209.07399","title":"A Light Recipe to Train Robust Vision Transformers","created":"2022-09-15","cited_by_count":64},{"id":"2002.06789","title":"CAT: Customized Adversarial Training for Improved Robustness","created":"2020-02-17","cited_by_count":61},{"id":"1511.05122","title":"Adversarial Manipulation of Deep Representations","created":"2015-11-16","cited_by_count":56},{"id":"1912.11969","title":"Efficient Adversarial Training with Transferable Adversarial Examples","created":"2019-12-27","cited_by_count":56},{"id":"1910.08051","title":"Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets","created":"2019-10-17","cited_by_count":55},{"id":"2105.00203","title":"Adversarial Example Detection for DNN Models: A Review and Experimental Comparison","created":"2021-05-01","cited_by_count":52},{"id":"1906.06784","title":"Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Too Much Accuracy","created":"2019-06-16","cited_by_count":49},{"id":"1811.10716","title":"Bilateral Adversarial Training: Towards Fast Training of More Robust Models Against Adversarial Attacks","created":"2018-11-26","cited_by_count":48},{"id":"2010.11828","title":"Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free","created":"2020-10-22","cited_by_count":48},{"id":"1910.09338","title":"An Alternative Surrogate Loss for PGD-based Adversarial Testing","created":"2019-10-21","cited_by_count":45},{"id":"2002.10025","title":"Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference","created":"2020-02-24","cited_by_count":45},{"id":"2210.07394","title":"Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation","created":"2022-10-13","cited_by_count":45},{"id":"1905.12105","title":"Certifiably Robust Interpretation in Deep Learning","created":"2019-05-28","cited_by_count":35},{"id":"2207.08977","title":"Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift","created":"2022-07-18","cited_by_count":34},{"id":"1907.05681","title":"Adversarial Lipschitz Regularization","created":"2019-07-12","cited_by_count":30},{"id":"2201.01965","title":"Efficient Global Optimization of Two-Layer ReLU Networks: Quadratic-Time Algorithms and Adversarial Training","created":"2022-01-06","cited_by_count":15},{"id":"2004.00570","title":"Tightened Convex Relaxations for Neural Network Robustness Certification","created":"2020-04-01","cited_by_count":14},{"id":"2103.05354","title":"Revisiting Model's Uncertainty and Confidences for Adversarial Example Detection","created":"2021-03-09","cited_by_count":9},{"id":"2107.11275","title":"A Differentiable Language Model Adversarial Attack on Text Classifiers","created":"2021-07-23","cited_by_count":9},{"id":"2010.01171","title":"Data-Driven Certification of Neural Networks with Random Input Noise","created":"2020-10-02","cited_by_count":8},{"id":"2211.08008","title":"MORA: Improving Ensemble Robustness Evaluation with Model-Reweighing Attack","created":"2022-11-15","cited_by_count":7},{"id":"2010.08603","title":"A Sequential Framework Towards an Exact SDP Verification of Neural Networks","created":"2020-10-16","cited_by_count":6},{"id":"2105.09685","title":"Simple Transparent Adversarial Examples","created":"2021-05-20","cited_by_count":6},{"id":"2204.08726","title":"Jacobian Ensembles Improve Robustness Trade-offs to Adversarial Attacks","created":"2022-04-19","cited_by_count":4},{"id":"2208.08697","title":"Resisting Adversarial Attacks in Deep Neural Networks using Diverse Decision Boundaries","created":"2022-08-18","cited_by_count":4},{"id":"2005.05750","title":"Evaluating Ensemble Robustness Against Adversarial Attacks","created":"2020-05-12","cited_by_count":2}],"next_cursor":null}