{"total":55,"rows":[{"id":"2302.14301","title":"A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking","created":"2023-02-28","cited_by_count":85,"area":null},{"id":"2206.08675","title":"Understanding Robust Overfitting of Adversarial Training and Beyond","created":"2022-06-17","cited_by_count":51,"area":null},{"id":"2210.15318","title":"Efficient and Effective Augmentation Strategy for Adversarial Training","created":"2022-10-27","cited_by_count":43,"area":null},{"id":"2301.09879","title":"Data Augmentation Alone Can Improve Adversarial Training","created":"2023-01-24","cited_by_count":43,"area":null},{"id":"2202.09844","title":"Sparsity Winning Twice: Better Robust Generalization from More Efficient Training","created":"2022-02-20","cited_by_count":41,"area":null},{"id":"2307.07873","title":"Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability","created":"2023-07-15","cited_by_count":32,"area":null},{"id":"2210.15291","title":"Isometric 3D Adversarial Examples in the Physical World","created":"2022-10-27","cited_by_count":25,"area":null},{"id":"2211.00525","title":"The Enemy of My Enemy is My Friend: Exploring Inverse Adversaries for Improving Adversarial Training","created":"2022-11-01","cited_by_count":23,"area":null},{"id":"2212.04985","title":"Understanding and Combating Robust Overfitting via Input Loss Landscape Analysis and Regularization","created":"2022-12-09","cited_by_count":22,"area":null},{"id":"2310.08847","title":"On the Over-Memorization During Natural, Robust and Catastrophic Overfitting","created":"2023-10-13","cited_by_count":20,"area":null},{"id":"2310.19360","title":"Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective","created":"2023-10-30","cited_by_count":18,"area":null},{"id":"2406.03880","title":"Memorization in deep learning: A survey","created":"2024-06-06","cited_by_count":15,"area":null},{"id":"2110.03135","title":"Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting","created":"2021-10-07","cited_by_count":14,"area":null},{"id":"2306.11035","title":"Adversarial Training Should Be Cast as a Non-Zero-Sum Game","created":"2023-06-19","cited_by_count":13,"area":null},{"id":"2112.15250","title":"Benign Overfitting in Adversarially Robust Linear Classification","created":"2021-12-31","cited_by_count":11,"area":null},{"id":"2303.02251","title":"Certified Robust Neural Networks: Generalization and Corruption Resistance","created":"2023-03-03","cited_by_count":11,"area":null},{"id":"2309.05145","title":"Outlier Robust Adversarial Training","created":"2023-09-10","cited_by_count":10,"area":null},{"id":"2306.14126","title":"Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training","created":"2023-06-25","cited_by_count":9,"area":null},{"id":"2207.10283","title":"One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training","created":"2022-07-21","cited_by_count":7,"area":null},{"id":"2305.12118","title":"Annealing Self-Distillation Rectification Improves Adversarial Training","created":"2023-05-20","cited_by_count":7,"area":"distill"},{"id":"2306.07197","title":"AROID: Improving Adversarial Robustness Through Online Instance-Wise Data Augmentation","created":"2023-06-12","cited_by_count":6,"area":null},{"id":"2401.14948","title":"Conserve-Update-Revise to Cure Generalization and Robustness Trade-off in Adversarial Training","created":"2024-01-26","cited_by_count":6,"area":null},{"id":"2410.15042","title":"Adversarial Training: A Survey","created":"2024-10-19","cited_by_count":6,"area":null},{"id":"2503.07501","title":"Trustworthy Machine Learning via Memorization and the Granular Long-Tail: A Survey on Interactions, Tradeoffs, and Beyond","created":"2025-03-10","cited_by_count":6,"area":null},{"id":"2306.01271","title":"On the Clean Generalization and Robust Overfitting in Adversarial Training from Two Theoretical Views: Representation Complexity and Training Dynamics","created":"2023-06-02","cited_by_count":5,"area":null},{"id":"2106.12900","title":"Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification Tasks","created":"2021-06-22","cited_by_count":4,"area":null},{"id":"2205.11744","title":"Alleviating Robust Overfitting of Adversarial Training With Consistency Regularization","created":"2022-05-24","cited_by_count":4,"area":null},{"id":"2211.00269","title":"Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks","created":"2022-11-01","cited_by_count":4,"area":null},{"id":"2211.16316","title":"A3T: Accuracy Aware Adversarial Training","created":"2022-11-29","cited_by_count":4,"area":null},{"id":"2306.14275","title":"Enhancing Adversarial Training via Reweighting Optimization Trajectory","created":"2023-06-25","cited_by_count":4,"area":null},{"id":"2406.05532","title":"Exploring Adversarial Robustness of Deep State Space Models","created":"2024-06-08","cited_by_count":4,"area":null},{"id":"2108.11135","title":"Bridged Adversarial Training","created":"2021-08-25","cited_by_count":3,"area":null},{"id":"2210.01288","title":"Strength-Adaptive Adversarial Training","created":"2022-10-04","cited_by_count":3,"area":null},{"id":"2311.00428","title":"NEO-KD: Knowledge-Distillation-Based Adversarial Training for Robust Multi-Exit Neural Networks","created":"2023-11-01","cited_by_count":3,"area":"distill"},{"id":"2508.02186","title":"Failure Cases Are Better Learned But Boundary Says Sorry: Facilitating Smooth Perception Change for Accuracy-Robustness Trade-Off in Adversarial Training","created":"2025-08-04","cited_by_count":3,"area":null},{"id":"2402.11733","title":"The Effectiveness of Random Forgetting for Robust Generalization","created":"2024-02-18","cited_by_count":2,"area":null},{"id":"2410.12671","title":"New Paradigm of Adversarial Training: Releasing Accuracy-Robustness Trade-Off via Dummy Class","created":"2024-10-16","cited_by_count":2,"area":null},{"id":"2505.22486","title":"Understanding Adversarial Training with Energy-based Models","created":"2025-05-28","cited_by_count":2,"area":null},{"id":"2310.00607","title":"Understanding Robust Overfitting from the Feature Generalization Perspective","created":"2023-10-01","cited_by_count":1,"area":null},{"id":"2401.13624","title":"Can overfitted deep neural networks in adversarial training generalize? -- An approximation viewpoint","created":"2024-01-24","cited_by_count":1,"area":null},{"id":"2410.07675","title":"Adversarial Robustness Overestimation and Instability in TRADES","created":"2024-10-10","cited_by_count":1,"area":null},{"id":"2511.00836","title":"Parameter Interpolation Adversarial Training for Robust Image Classification","created":"2025-11-02","cited_by_count":1,"area":null},{"id":"2211.14088","title":"Boundary Adversarial Examples Against Adversarial Overfitting","created":"2022-11-25","cited_by_count":0,"area":null},{"id":"2303.13955","title":"PIAT: Parameter Interpolation based Adversarial Training for Image Classification","created":"2023-03-24","cited_by_count":0,"area":null},{"id":"2306.07613","title":"Revisiting and Advancing Adversarial Training Through A Simple Baseline","created":"2023-06-13","cited_by_count":0,"area":null},{"id":"2307.08596","title":"Omnipotent Adversarial Training in the Wild","created":"2023-07-14","cited_by_count":0,"area":null},{"id":"2310.03358","title":"Enhancing Robust Representation in Adversarial Training: Alignment and Exclusion Criteria","created":"2023-10-05","cited_by_count":0,"area":null},{"id":"2310.12713","title":"Learn from the Past: A Proxy Guided Adversarial Defense Framework with Self Distillation Regularization","created":"2023-10-19","cited_by_count":0,"area":"distill"},{"id":"2311.16526","title":"On robust overfitting: adversarial training induced distribution matters","created":"2023-11-28","cited_by_count":0,"area":null},{"id":"2312.02236","title":"Rethinking Adversarial Training with Neural Tangent Kernel","created":"2023-12-04","cited_by_count":0,"area":null},{"id":"2312.02237","title":"Singular Regularization with Information Bottleneck Improves Model's Adversarial Robustness","created":"2023-12-04","cited_by_count":0,"area":null},{"id":"2503.04315","title":"Provable Robust Overfitting Mitigation in Wasserstein Distributionally Robust Optimization","created":"2025-03-06","cited_by_count":0,"area":null},{"id":"2506.05032","title":"Identifying and Understanding Cross-Class Features in Adversarial Training","created":"2025-06-05","cited_by_count":0,"area":null},{"id":"2506.19680","title":"Model Guidance via Robust Feature Attribution","created":"2025-06-24","cited_by_count":0,"area":null},{"id":"2604.26496","title":"Robust Alignment: Harmonizing Clean Accuracy and Adversarial Robustness in Adversarial Training","created":"2026-04-29","cited_by_count":0,"area":null}],"next_cursor":null}