{"total":84,"rows":[{"id":"2102.01356","title":"Recent Advances in Adversarial Training for Adversarial Robustness","created":"2021-02-02","cited_by_count":279,"area":null},{"id":"2304.07288","title":"Cross-Entropy Loss Functions: Theoretical Analysis and Applications","created":"2023-04-14","cited_by_count":210,"area":null},{"id":"2112.08304","title":"On the Convergence and Robustness of Adversarial Training","created":"2021-12-15","cited_by_count":130,"area":null},{"id":"2203.06616","title":"LAS-AT: Adversarial Training with Learnable Attack Strategy","created":"2022-03-13","cited_by_count":95,"area":null},{"id":"2207.12391","title":"SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness","created":"2022-07-25","cited_by_count":74,"area":null},{"id":"2102.04716","title":"Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training","created":"2021-02-09","cited_by_count":59,"area":null},{"id":"2303.14460","title":"CFA: Class-wise Calibrated Fair Adversarial Training","created":"2023-03-25","cited_by_count":53,"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":"2111.12621","title":"Accelerating Deep Learning with Dynamic Data Pruning","created":"2021-11-24","cited_by_count":43,"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":"2308.02897","title":"An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial Transferability","created":"2023-08-05","cited_by_count":42,"area":null},{"id":"2103.04302","title":"Detecting Adversarial Examples from Sensitivity Inconsistency of Spatial-Transform Domain","created":"2021-03-07","cited_by_count":29,"area":null},{"id":"2109.09320","title":"Robust Physical-World Attacks on Face Recognition","created":"2021-09-20","cited_by_count":26,"area":null},{"id":"2110.09714","title":"Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information","created":"2021-10-19","cited_by_count":25,"area":null},{"id":"2106.06235","title":"Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks","created":"2021-06-11","cited_by_count":24,"area":null},{"id":"2102.05311","title":"CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection","created":"2021-02-10","cited_by_count":21,"area":null},{"id":"2406.05927","title":"MeanSparse: Post-Training Robustness Enhancement Through Mean-Centered Feature Sparsification","created":"2024-06-09","cited_by_count":21,"area":null},{"id":"2102.03482","title":"Understanding the Interaction of Adversarial Training with Noisy Labels","created":"2021-02-06","cited_by_count":19,"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":"2303.13846","title":"Feature Separation and Recalibration for Adversarial Robustness","created":"2023-03-24","cited_by_count":16,"area":null},{"id":"2206.10057","title":"Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum","created":"2022-06-21","cited_by_count":15,"area":null},{"id":"2105.14785","title":"Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart","created":"2021-05-31","cited_by_count":14,"area":null},{"id":"2107.04263","title":"Towards Robust General Medical Image Segmentation","created":"2021-07-09","cited_by_count":14,"area":null},{"id":"2308.11443","title":"Revisiting and Exploring Efficient Fast Adversarial Training via LAW: Lipschitz Regularization and Auto Weight Averaging","created":"2023-08-22","cited_by_count":14,"area":null},{"id":"2312.08890","title":"Defenses in Adversarial Machine Learning: A Survey","created":"2023-12-13","cited_by_count":14,"area":null},{"id":"2108.06885","title":"Neural Architecture Dilation for Adversarial Robustness","created":"2021-08-16","cited_by_count":13,"area":null},{"id":"2201.12765","title":"Improving Robustness by Enhancing Weak Subnets","created":"2022-01-30","cited_by_count":13,"area":null},{"id":"2302.12366","title":"Less is More: Data Pruning for Faster Adversarial Training","created":"2023-02-23","cited_by_count":13,"area":null},{"id":"2102.01886","title":"Learning Diverse-Structured Networks for Adversarial Robustness","created":"2021-02-03","cited_by_count":12,"area":null},{"id":"2206.00700","title":"RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Model","created":"2022-06-01","cited_by_count":11,"area":null},{"id":"2206.07953","title":"Analysis and Extensions of Adversarial Training for Video Classification","created":"2022-06-16","cited_by_count":11,"area":null},{"id":"2203.01439","title":"Enhancing Adversarial Robustness for Deep Metric Learning","created":"2022-03-02","cited_by_count":10,"area":null},{"id":"2102.07327","title":"Guided Interpolation for Adversarial Training","created":"2021-02-15","cited_by_count":8,"area":null},{"id":"2105.14676","title":"NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels","created":"2021-05-31","cited_by_count":8,"area":null},{"id":"2102.05241","title":"Detecting Localized Adversarial Examples: A Generic Approach using Critical Region Analysis","created":"2021-02-10","cited_by_count":7,"area":null},{"id":"2104.00322","title":"Domain Invariant Adversarial Learning","created":"2021-04-01","cited_by_count":7,"area":null},{"id":"2308.16454","title":"Adversarial Finetuning with Latent Representation Constraint to Mitigate Accuracy-Robustness Tradeoff","created":"2023-08-31","cited_by_count":7,"area":null},{"id":"2108.00402","title":"Style Curriculum Learning for Robust Medical Image Segmentation","created":"2021-08-01","cited_by_count":6,"area":null},{"id":"2112.00323","title":"Push Stricter to Decide Better: A Class-Conditional Feature Adaptive Framework for Improving Adversarial Robustness","created":"2021-12-01","cited_by_count":6,"area":null},{"id":"2406.00685","title":"Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training","created":"2024-06-02","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":"2310.02480","title":"Splitting the Difference on Adversarial Training","created":"2023-10-03","cited_by_count":5,"area":null},{"id":"2502.01896","title":"INTACT: Inducing Noise Tolerance through Adversarial Curriculum Training for LiDAR-based Safety-Critical Perception and Autonomy","created":"2025-02-04","cited_by_count":5,"area":null},{"id":"2111.00295","title":"Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided Curriculum Learning Approach","created":"2021-10-30","cited_by_count":4,"area":null},{"id":"2206.01736","title":"Adaptive Adversarial Training to Improve Adversarial Robustness of DNNs for Medical Image Segmentation and Detection","created":"2022-06-02","cited_by_count":4,"area":null},{"id":"2209.03148","title":"Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training","created":"2022-09-05","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":"2405.16918","title":"The Uncanny Valley: Exploring Adversarial Robustness from a Flatness Perspective","created":"2024-05-27","cited_by_count":4,"area":null},{"id":"2203.08739","title":"What Do Adversarially trained Neural Networks Focus: A Fourier Domain-based Study","created":"2022-03-16","cited_by_count":3,"area":null},{"id":"2206.12169","title":"AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems","created":"2022-06-24","cited_by_count":3,"area":null},{"id":"2207.13192","title":"Perception-Aware Attack: Creating Adversarial Music via Reverse-Engineering Human Perception","created":"2022-07-26","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":"2306.02618","title":"Enhance Diffusion to Improve Robust Generalization","created":"2023-06-05","cited_by_count":3,"area":null},{"id":"2510.14231","title":"When Flatness Does (Not) Guarantee Adversarial Robustness","created":"2025-10-16","cited_by_count":3,"area":null},{"id":"2103.03076","title":"Dynamic Efficient Adversarial Training Guided by Gradient Magnitude","created":"2021-03-04","cited_by_count":2,"area":null},{"id":"2202.03077","title":"Adversarial Attack and Defense for Non-Parametric Two-Sample Tests","created":"2022-02-07","cited_by_count":2,"area":null},{"id":"2510.03224","title":"Test-Time Defense Against Adversarial Attacks via Stochastic Resonance of Latent Ensembles","created":"2025-10-03","cited_by_count":2,"area":null},{"id":"2106.15776","title":"Local Reweighting for Adversarial Training","created":"2021-06-30","cited_by_count":1,"area":null},{"id":"2110.00623","title":"Calibrated Adversarial Training","created":"2021-10-01","cited_by_count":1,"area":null},{"id":"2206.00489","title":"Attack-Agnostic Adversarial Detection","created":"2022-06-01","cited_by_count":1,"area":null},{"id":"2306.12582","title":"Adversarial Training with Generated Data in High-Dimensional Regression: An Asymptotic Study","created":"2023-06-21","cited_by_count":1,"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":"2401.11406","title":"Adversarial Augmentation Training Makes Action Recognition Models More Robust to Realistic Video Distribution Shifts","created":"2024-01-21","cited_by_count":1,"area":null},{"id":"2410.16579","title":"Conflict-Aware Adversarial Training","created":"2024-10-21","cited_by_count":1,"area":null},{"id":"2601.04991","title":"Higher-Order Adversarial Patches for Real-Time Object Detectors","created":"2026-01-08","cited_by_count":1,"area":null},{"id":"2201.12469","title":"ScaLA: Accelerating Adaptation of Pre-Trained Transformer-Based Language Models via Efficient Large-Batch Adversarial Noise","created":"2022-01-29","cited_by_count":0,"area":null},{"id":"2204.07024","title":"Q-TART: Quickly Training for Adversarial Robustness and in-Transferability","created":"2022-04-14","cited_by_count":0,"area":null},{"id":"2205.02741","title":"CE-based white-box adversarial attacks will not work using super-fitting","created":"2022-05-04","cited_by_count":0,"area":null},{"id":"2212.14115","title":"Certifying Safety in Reinforcement Learning under Adversarial Perturbation Attacks","created":"2022-12-28","cited_by_count":0,"area":null},{"id":"2301.01495","title":"Beckman Defense","created":"2023-01-04","cited_by_count":0,"area":null},{"id":"2308.15072","title":"Advancing Adversarial Robustness Through Adversarial Logit Update","created":"2023-08-29","cited_by_count":0,"area":null},{"id":"2309.09464","title":"Reducing Adversarial Training Cost with Gradient Approximation","created":"2023-09-18","cited_by_count":0,"area":null},{"id":"2401.15248","title":"Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective","created":"2024-01-26","cited_by_count":0,"area":null},{"id":"2403.01896","title":"Robustness bounds on the successful adversarial examples in probabilistic models: Implications from Gaussian processes","created":"2024-03-04","cited_by_count":0,"area":null},{"id":"2411.17959","title":"Adversarial Training in Low-Label Regimes with Margin-Based Interpolation","created":"2024-11-27","cited_by_count":0,"area":null},{"id":"2503.11452","title":"Deep Learning Agents Trained For Avoidance Behave Like Hawks And Doves","created":"2025-03-14","cited_by_count":0,"area":null},{"id":"2504.10850","title":"How to Enhance Downstream Adversarial Robustness (almost) without Touching the Pre-Trained Foundation Model?","created":"2025-04-15","cited_by_count":0,"area":null},{"id":"2506.12733","title":"Learning to Fuse: Modality-Aware Adaptive Scheduling for Robust Multimodal Foundation Models","created":"2025-06-15","cited_by_count":0,"area":null},{"id":"2506.15685","title":"Ignition Phase : Standard Training for Fast Adversarial Robustness","created":"2025-05-25","cited_by_count":0,"area":null},{"id":"2507.06890","title":"A Single-Point Measurement Framework for Robust Cyber-Attack Diagnosis in Smart Microgrids Using Dual Fractional-Order Feature Analysis","created":"2025-07-09","cited_by_count":0,"area":null},{"id":"2509.16833","title":"SOLAR: Switchable Output Layer for Accuracy and Robustness in Once-for-All Training","created":"2025-09-20","cited_by_count":0,"area":null},{"id":"2509.23325","title":"Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Epsilon-Scheduling","created":"2025-09-27","cited_by_count":0,"area":null},{"id":"2603.27556","title":"Towards Domain-Generalized Open-Vocabulary Object Detection: A Progressive Domain-invariant Cross-modal Alignment Method","created":"2026-03-29","cited_by_count":0,"area":null},{"id":"2609.02440","title":"Towards One-for-All Robustness Across a Continuum of Threat Levels","created":"2026-09-02","cited_by_count":0,"area":null}],"next_cursor":null}