{"list":"other","total":60,"rows":[{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"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":"1312.6199","title":"Intriguing properties of neural networks","created":"2013-12-21","cited_by_count":5155},{"id":"1907.05600","title":"Generative Modeling by Estimating Gradients of the Data Distribution","created":"2019-07-12","cited_by_count":4101},{"id":"1608.04644","title":"Towards Evaluating the Robustness of Neural Networks","created":"2016-08-16","cited_by_count":2998},{"id":"1606.03498","title":"Improved Techniques for Training GANs","created":"2016-06-10","cited_by_count":2970},{"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":"2010.01412","title":"Sharpness-Aware Minimization for Efficiently Improving Generalization","created":"2020-10-03","cited_by_count":1417},{"id":"1803.05407","title":"Averaging Weights Leads to Wider Optima and Better Generalization","created":"2018-03-14","cited_by_count":1209},{"id":"1705.07204","title":"Ensemble Adversarial Training: Attacks and Defenses","created":"2017-05-19","cited_by_count":840},{"id":"2001.03994","title":"Fast is better than free: Revisiting adversarial training","created":"2020-01-12","cited_by_count":727},{"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":"2002.11569","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":"1912.03263","title":"Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One","created":"2019-12-06","cited_by_count":405},{"id":"1804.11285","title":"Adversarially Robust Generalization Requires More Data","created":"2018-04-30","cited_by_count":318},{"id":"2002.11242","title":"Attacks Which Do Not Kill Training Make Adversarial Learning Stronger","created":"2020-02-26","cited_by_count":243},{"id":"2110.09468","title":"Improving Robustness using Generated Data","created":"2021-10-18","cited_by_count":234},{"id":"1803.06373","title":"Adversarial Logit Pairing","created":"2018-03-16","cited_by_count":224},{"id":"2007.02617","title":"Understanding and Improving Fast Adversarial Training","created":"2020-07-06","cited_by_count":214},{"id":"2302.04638","title":"Better Diffusion Models Further Improve Adversarial Training","created":"2023-02-09","cited_by_count":208},{"id":"2010.01736","title":"Geometry-aware Instance-reweighted Adversarial Training","created":"2020-10-05","cited_by_count":198},{"id":"1812.02637","title":"MMA Training: Direct Input Space Margin Maximization through Adversarial Training","created":"2018-12-06","cited_by_count":172},{"id":"1905.13725","title":"Are Labels Required for Improving Adversarial Robustness?","created":"2019-05-31","cited_by_count":163},{"id":"2012.08125","title":"Learning Energy-Based Models by Diffusion Recovery Likelihood","created":"2020-12-15","cited_by_count":123},{"id":"1906.00555","title":"Adversarially Robust Generalization Just Requires More Unlabeled Data","created":"2019-06-03","cited_by_count":76},{"id":"2010.01799","title":"Understanding Catastrophic Overfitting in Single-step Adversarial Training","created":"2020-10-05","cited_by_count":76},{"id":"2305.16494","title":"Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability","created":"2023-05-25","cited_by_count":66},{"id":"2011.14969","title":"Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses","created":"2020-11-30","cited_by_count":62},{"id":"2305.13948","title":"Decoupled Kullback-Leibler Divergence Loss","created":"2023-05-23","cited_by_count":58},{"id":"2104.04448","title":"Relating Adversarially Robust Generalization to Flat Minima","created":"2021-04-09","cited_by_count":57},{"id":"2106.07904","title":"Probabilistic Margins for Instance Reweighting in Adversarial Training","created":"2021-06-15","cited_by_count":57},{"id":"2106.01606","title":"Exploring Memorization in Adversarial Training","created":"2021-06-03","cited_by_count":55},{"id":"2206.08675","title":"Understanding Robust Overfitting of Adversarial Training and Beyond","created":"2022-06-17","cited_by_count":51},{"id":"2308.16258","title":"Robust Principles: Architectural Design Principles for Adversarially Robust CNNs","created":"2023-08-30","cited_by_count":46},{"id":"2202.01181","title":"Make Some Noise: Reliable and Efficient Single-Step Adversarial Training","created":"2022-02-02","cited_by_count":43},{"id":"2004.08628","title":"Single-step Adversarial training with Dropout Scheduling","created":"2020-04-18","cited_by_count":41},{"id":"2111.12229","title":"Subspace Adversarial Training","created":"2021-11-24","cited_by_count":41},{"id":"2202.09844","title":"Sparsity Winning Twice: Better Robust Generalization from More Efficient Training","created":"2022-02-20","cited_by_count":41},{"id":"2102.07861","title":"Low Curvature Activations Reduce Overfitting in Adversarial Training","created":"2021-02-15","cited_by_count":29},{"id":"2104.01575","title":"Reliably fast adversarial training via latent adversarial perturbation","created":"2021-04-04","cited_by_count":29},{"id":"2109.09032","title":"JEM++: Improved Techniques for Training JEM","created":"2021-09-19","cited_by_count":28},{"id":"2108.09093","title":"Towards Understanding the Generative Capability of Adversarially Robust Classifiers","created":"2021-08-20","cited_by_count":26},{"id":"2206.02417","title":"Fast Adversarial Training with Adaptive Step Size","created":"2022-06-06","cited_by_count":25},{"id":"2310.08847","title":"On the Over-Memorization During Natural, Robust and Catastrophic Overfitting","created":"2023-10-13","cited_by_count":20},{"id":"2209.07959","title":"Towards Bridging the Performance Gaps of Joint Energy-based Models","created":"2022-09-16","cited_by_count":15},{"id":"2203.13455","title":"A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training","created":"2022-03-25","cited_by_count":13},{"id":"2103.01914","title":"Evaluating the Robustness of Geometry-Aware Instance-Reweighted Adversarial Training","created":"2021-03-02","cited_by_count":12},{"id":"2404.08154","title":"Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization","created":"2024-04-11","cited_by_count":12},{"id":"2407.06315","title":"Shedding More Light on Robust Classifiers under the lens of Energy-based Models","created":"2024-07-08","cited_by_count":9},{"id":"2106.06927","title":"Inverting Adversarially Robust Networks for Image Synthesis","created":"2021-06-13","cited_by_count":6},{"id":"2209.11549","title":"MAGIC: Mask-Guided Image Synthesis by Inverting a Quasi-Robust Classifier","created":"2022-09-23","cited_by_count":6},{"id":"2303.04343","title":"M-EBM: Towards Understanding the Manifolds of Energy-Based Models","created":"2023-03-08","cited_by_count":6},{"id":"2306.14275","title":"Enhancing Adversarial Training via Reweighting Optimization Trajectory","created":"2023-06-25","cited_by_count":4},{"id":"2304.04033","title":"Exploring the Connection between Robust and Generative Models","created":"2023-04-08","cited_by_count":2}],"next_cursor":null}