{"total":133,"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":"2103.01946","title":"Fixing Data Augmentation to Improve Adversarial Robustness","created":"2021-03-02","cited_by_count":204,"area":null},{"id":"2111.05328","title":"Data Augmentation Can Improve Robustness","created":"2021-11-09","cited_by_count":194,"area":null},{"id":"2110.03141","title":"Efficient Sharpness-aware Minimization for Improved Training of Neural Networks","created":"2021-10-07","cited_by_count":137,"area":null},{"id":"2103.08896","title":"Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation","created":"2021-03-16","cited_by_count":134,"area":null},{"id":"2102.08452","title":"Globally-Robust Neural Networks","created":"2021-02-16","cited_by_count":108,"area":null},{"id":"2204.05255","title":"Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information","created":"2022-04-11","cited_by_count":103,"area":null},{"id":"2104.04448","title":"Relating Adversarially Robust Generalization to Flat Minima","created":"2021-04-09","cited_by_count":57,"area":null},{"id":"2112.11313","title":"On the Adversarial Robustness of Causal Algorithmic Recourse","created":"2021-12-21","cited_by_count":56,"area":null},{"id":"2205.00656","title":"Debiased Contrastive Learning of Unsupervised Sentence Representations","created":"2022-05-02","cited_by_count":56,"area":null},{"id":"2106.01606","title":"Exploring Memorization in Adversarial Training","created":"2021-06-03","cited_by_count":55,"area":null},{"id":"2402.15555","title":"Deep Networks Always Grok and Here is Why","created":"2024-02-23","cited_by_count":51,"area":null},{"id":"2103.04623","title":"Consistency Regularization for Adversarial Robustness","created":"2021-03-08","cited_by_count":42,"area":null},{"id":"2111.15121","title":"Pyramid Adversarial Training Improves ViT Performance","created":"2021-11-30","cited_by_count":39,"area":null},{"id":"2209.13083","title":"Why neural networks find simple solutions: the many regularizers of geometric complexity","created":"2022-09-27","cited_by_count":39,"area":null},{"id":"2212.05630","title":"DISCO: Adversarial Defense with Local Implicit Functions","created":"2022-12-11","cited_by_count":34,"area":null},{"id":"2106.04938","title":"Attacking Adversarial Attacks as A Defense","created":"2021-06-09","cited_by_count":32,"area":null},{"id":"2204.02735","title":"Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck","created":"2022-04-06","cited_by_count":30,"area":null},{"id":"2207.10498","title":"Towards Efficient Adversarial Training on Vision Transformers","created":"2022-07-21","cited_by_count":30,"area":null},{"id":"2104.01575","title":"Reliably fast adversarial training via latent adversarial perturbation","created":"2021-04-04","cited_by_count":29,"area":null},{"id":"2104.01086","title":"Defending Against Image Corruptions Through Adversarial Augmentations","created":"2021-04-02","cited_by_count":28,"area":null},{"id":"2108.01289","title":"AdvRush: Searching for Adversarially Robust Neural Architectures","created":"2021-08-03","cited_by_count":28,"area":null},{"id":"2212.11005","title":"Revisiting Residual Networks for Adversarial Robustness: An Architectural Perspective","created":"2022-12-21","cited_by_count":28,"area":null},{"id":"2104.03863","title":"A single gradient step finds adversarial examples on random two-layers neural networks","created":"2021-04-08","cited_by_count":26,"area":null},{"id":"2106.12611","title":"Adversarial Examples in Multi-Layer Random ReLU Networks","created":"2021-06-23","cited_by_count":26,"area":null},{"id":"2104.09284","title":"LAFEAT: Piercing Through Adversarial Defenses with Latent Features","created":"2021-04-19","cited_by_count":24,"area":null},{"id":"2110.05626","title":"Parameterizing Activation Functions for Adversarial Robustness","created":"2021-10-11","cited_by_count":22,"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":"2207.06202","title":"Adversarially-Aware Robust Object Detector","created":"2022-07-13","cited_by_count":20,"area":null},{"id":"2206.07144","title":"Efficiently Training Low-Curvature Neural Networks","created":"2022-06-14","cited_by_count":19,"area":null},{"id":"2306.01505","title":"Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations","created":"2023-06-02","cited_by_count":19,"area":null},{"id":"2407.14257","title":"SparseCraft: Few-Shot Neural Reconstruction through Stereopsis Guided Geometric Linearization","created":"2024-07-19","cited_by_count":19,"area":null},{"id":"2202.03898","title":"Verification-Aided Deep Ensemble Selection","created":"2022-02-08","cited_by_count":18,"area":null},{"id":"2303.11135","title":"TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization","created":"2023-03-20","cited_by_count":18,"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":"2209.07534","title":"Improving Robust Fairness via Balance Adversarial Training","created":"2022-09-15","cited_by_count":17,"area":null},{"id":"2207.11378","title":"Do Perceptually Aligned Gradients Imply Adversarial Robustness?","created":"2022-07-22","cited_by_count":15,"area":null},{"id":"2302.01409","title":"Hyperbolic Contrastive Learning","created":"2023-02-02","cited_by_count":15,"area":null},{"id":"2105.11363","title":"Learning Security Classifiers with Verified Global Robustness Properties","created":"2021-05-24","cited_by_count":14,"area":null},{"id":"2306.03048","title":"From Robustness to Explainability and Back Again","created":"2023-06-05","cited_by_count":14,"area":null},{"id":"2310.18477","title":"Understanding and Improving Ensemble Adversarial Defense","created":"2023-10-27","cited_by_count":14,"area":null},{"id":"2203.17209","title":"Adversarial Examples in Random Neural Networks with General Activations","created":"2022-03-31","cited_by_count":13,"area":null},{"id":"2310.10010","title":"Black-box Targeted Adversarial Attack on Segment Anything (SAM)","created":"2023-10-16","cited_by_count":13,"area":null},{"id":"2407.12443","title":"Preventing Catastrophic Overfitting in Fast Adversarial Training: A Bi-level Optimization Perspective","created":"2024-07-17","cited_by_count":13,"area":null},{"id":"2103.14332","title":"Building Reliable Explanations of Unreliable Neural Networks: Locally Smoothing Perspective of Model Interpretation","created":"2021-03-26","cited_by_count":12,"area":null},{"id":"2105.02942","title":"Understanding Catastrophic Overfitting in Adversarial Training","created":"2021-05-06","cited_by_count":12,"area":null},{"id":"2204.04890","title":"Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization","created":"2022-04-11","cited_by_count":12,"area":null},{"id":"2307.09763","title":"Towards Building More Robust Models with Frequency Bias","created":"2023-07-19","cited_by_count":12,"area":null},{"id":"2103.07470","title":"Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers","created":"2021-03-15","cited_by_count":11,"area":null},{"id":"2205.04533","title":"How Does Frequency Bias Affect the Robustness of Neural Image Classifiers against Common Corruption and Adversarial Perturbations?","created":"2022-05-09","cited_by_count":11,"area":null},{"id":"2209.09117","title":"Part-Based Models Improve Adversarial Robustness","created":"2022-09-15","cited_by_count":11,"area":null},{"id":"2101.10531","title":"Deep Learning for Scene Classification: A Survey","created":"2021-01-26","cited_by_count":9,"area":null},{"id":"2206.06257","title":"Distributed Adversarial Training to Robustify Deep Neural Networks at Scale","created":"2022-06-13","cited_by_count":9,"area":null},{"id":"2209.05785","title":"Adversarial Coreset Selection for Efficient Robust Training","created":"2022-09-13","cited_by_count":9,"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":"2106.03614","title":"Adversarial Attack and Defense in Deep Ranking","created":"2021-06-07","cited_by_count":8,"area":null},{"id":"2210.12606","title":"Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games","created":"2022-10-23","cited_by_count":8,"area":null},{"id":"2401.11618","title":"Efficient local linearity regularization to overcome catastrophic overfitting","created":"2024-01-21","cited_by_count":8,"area":null},{"id":"2405.08886","title":"The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks","created":"2024-05-14","cited_by_count":8,"area":null},{"id":"2101.12372","title":"Adversarial Learning with Cost-Sensitive Classes","created":"2021-01-29","cited_by_count":6,"area":null},{"id":"2207.07032","title":"Adversarial Attacks on Monocular Pose Estimation","created":"2022-07-14","cited_by_count":6,"area":null},{"id":"2308.06887","title":"Robustified ANNs Reveal Wormholes Between Human Category Percepts","created":"2023-08-14","cited_by_count":6,"area":null},{"id":"2312.03286","title":"Indirect Gradient Matching for Adversarial Robust Distillation","created":"2023-12-06","cited_by_count":6,"area":null},{"id":"2409.20139","title":"Characterizing Model Robustness via Natural Input Gradients","created":"2024-09-30","cited_by_count":6,"area":null},{"id":"2102.08868","title":"Bridging the Gap Between Adversarial Robustness and Optimization Bias","created":"2021-02-17","cited_by_count":5,"area":null},{"id":"2109.05793","title":"Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models","created":"2021-09-13","cited_by_count":5,"area":null},{"id":"2306.07104","title":"Unveiling the Hessian's Connection to the Decision Boundary","created":"2023-06-12","cited_by_count":5,"area":null},{"id":"2312.13027","title":"Doubly Perturbed Task Free Continual Learning","created":"2023-12-20","cited_by_count":5,"area":null},{"id":"2409.03807","title":"Accelerate Neural Subspace-Based Reduced-Order Solver of Deformable Simulation by Lipschitz Optimization","created":"2024-09-05","cited_by_count":5,"area":null},{"id":"2103.00778","title":"Explaining Adversarial Vulnerability with a Data Sparsity Hypothesis","created":"2021-03-01","cited_by_count":4,"area":null},{"id":"2107.05712","title":"A Closer Look at the Adversarial Robustness of Information Bottleneck Models","created":"2021-07-12","cited_by_count":4,"area":null},{"id":"2109.05105","title":"Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models","created":"2021-09-10","cited_by_count":4,"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":"2206.11228","title":"Adversarially trained neural representations may already be as robust as corresponding biological neural representations","created":"2022-06-19","cited_by_count":4,"area":null},{"id":"2210.05118","title":"Boosting Adversarial Robustness From The Perspective of Effective Margin Regularization","created":"2022-10-11","cited_by_count":4,"area":null},{"id":"2211.15900","title":"Towards More Robust Interpretation via Local Gradient Alignment","created":"2022-11-29","cited_by_count":4,"area":null},{"id":"2306.03430","title":"Revisiting the Trade-off between Accuracy and Robustness via Weight Distribution of Filters","created":"2023-06-06","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":"2308.08938","title":"Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces","created":"2023-08-17","cited_by_count":4,"area":null},{"id":"2412.06381","title":"Gentle Local Robustness implies Generalization","created":"2024-12-09","cited_by_count":4,"area":null},{"id":"2104.01231","title":"Diverse Gaussian Noise Consistency Regularization for Robustness and Uncertainty Calibration","created":"2021-04-02","cited_by_count":3,"area":null},{"id":"2303.13326","title":"Decentralized Adversarial Training over Graphs","created":"2023-03-23","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":"2407.16999","title":"SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing","created":"2024-07-24","cited_by_count":3,"area":null},{"id":"2103.01400","title":"Smoothness Analysis of Adversarial Training","created":"2021-03-02","cited_by_count":2,"area":null},{"id":"2109.01945","title":"Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness","created":"2021-09-04","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":"2205.09586","title":"On Trace of PGD-Like Adversarial Attacks","created":"2022-05-19","cited_by_count":2,"area":null},{"id":"2206.08242","title":"Catastrophic overfitting can be induced with discriminative non-robust features","created":"2022-06-16","cited_by_count":2,"area":null},{"id":"2207.13036","title":"Jacobian Norm with Selective Input Gradient Regularization for Improved and Interpretable Adversarial Defense","created":"2022-07-09","cited_by_count":2,"area":null},{"id":"2303.00340","title":"A Practical Upper Bound for the Worst-Case Attribution Deviations","created":"2023-03-01","cited_by_count":2,"area":null},{"id":"2303.01936","title":"Multi-Agent Adversarial Training Using Diffusion Learning","created":"2023-03-03","cited_by_count":2,"area":null},{"id":"2303.14922","title":"CAT:Collaborative Adversarial Training","created":"2023-03-27","cited_by_count":2,"area":null},{"id":"2311.13656","title":"Panda or not Panda? Understanding Adversarial Attacks with Interactive Visualization","created":"2023-11-22","cited_by_count":2,"area":null},{"id":"2401.07874","title":"Do stable neural networks exist for classification problems? -- A new view on stability in AI","created":"2024-01-15","cited_by_count":2,"area":null},{"id":"2511.12199","title":"MPD-SGR: Robust Spiking Neural Networks with Membrane Potential Distribution-Driven Surrogate Gradient Regularization","created":"2025-11-15","cited_by_count":2,"area":null},{"id":"2102.05096","title":"Towards Bridging the gap between Empirical and Certified Robustness against Adversarial Examples","created":"2021-02-09","cited_by_count":1,"area":null},{"id":"2103.13612","title":"THAT: Two Head Adversarial Training for Improving Robustness at Scale","created":"2021-03-25","cited_by_count":1,"area":null},{"id":"2106.10252","title":"Less is More: Feature Selection for Adversarial Robustness with Compressive Counter-Adversarial Attacks","created":"2021-06-18","cited_by_count":1,"area":null}],"next_cursor":"100"}