{"total":49,"rows":[{"id":"2112.00059","title":"Evaluating Gradient Inversion Attacks and Defenses in Federated Learning","created":"2021-11-30","cited_by_count":196,"area":null},{"id":"2102.00436","title":"Admix: Enhancing the Transferability of Adversarial Attacks","created":"2021-01-31","cited_by_count":156,"area":null},{"id":"2104.04448","title":"Relating Adversarially Robust Generalization to Flat Minima","created":"2021-04-09","cited_by_count":57,"area":null},{"id":"2111.09277","title":"SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness","created":"2021-11-17","cited_by_count":49,"area":null},{"id":"2212.10888","title":"A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability","created":"2022-12-21","cited_by_count":31,"area":null},{"id":"2110.02180","title":"Noisy Feature Mixup","created":"2021-10-05","cited_by_count":27,"area":null},{"id":"2106.07085","title":"Survey: Image Mixing and Deleting for Data Augmentation","created":"2021-06-13","cited_by_count":26,"area":null},{"id":"2209.07735","title":"Enhance the Visual Representation via Discrete Adversarial Training","created":"2022-09-16","cited_by_count":23,"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":"2409.05202","title":"A Survey on Mixup Augmentations and Beyond","created":"2024-09-08","cited_by_count":20,"area":null},{"id":"2110.07647","title":"Towards Understanding the Data Dependency of Mixup-style Training","created":"2021-10-14","cited_by_count":19,"area":null},{"id":"2301.12554","title":"Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing","created":"2023-01-29","cited_by_count":18,"area":null},{"id":"2202.01263","title":"NoisyMix: Boosting Model Robustness to Common Corruptions","created":"2022-02-02","cited_by_count":17,"area":null},{"id":"2402.02263","title":"MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers","created":"2024-02-03","cited_by_count":16,"area":null},{"id":"2203.02006","title":"Why adversarial training can hurt robust accuracy","created":"2022-03-03","cited_by_count":14,"area":null},{"id":"2210.13512","title":"Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup","created":"2022-10-24","cited_by_count":14,"area":null},{"id":"2303.13813","title":"Generalist: Decoupling Natural and Robust Generalization","created":"2023-03-24","cited_by_count":14,"area":null},{"id":"2201.12765","title":"Improving Robustness by Enhancing Weak Subnets","created":"2022-01-30","cited_by_count":13,"area":null},{"id":"2112.06323","title":"Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses","created":"2021-12-12","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":"2103.11589","title":"Adversarially Optimized Mixup for Robust Classification","created":"2021-03-22","cited_by_count":7,"area":null},{"id":"2204.07373","title":"Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning","created":"2022-04-15","cited_by_count":7,"area":null},{"id":"2204.08182","title":"Modality-Balanced Embedding for Video Retrieval","created":"2022-04-18","cited_by_count":6,"area":null},{"id":"2208.13838","title":"Towards Adversarial Purification using Denoising AutoEncoders","created":"2022-08-29","cited_by_count":6,"area":null},{"id":"2212.13381","title":"MixupE: Understanding and Improving Mixup from Directional Derivative Perspective","created":"2022-12-27","cited_by_count":6,"area":null},{"id":"2401.04986","title":"Structure-Preserving Physics-Informed Neural Networks With Energy or Lyapunov Structure","created":"2024-01-10","cited_by_count":6,"area":null},{"id":"2205.07315","title":"Learn2Weight: Parameter Adaptation against Similar-domain Adversarial Attacks","created":"2022-05-15","cited_by_count":5,"area":null},{"id":"2503.22984","title":"Optimal Transport-Guided Source-Free Adaptation for Face Anti-Spoofing","created":"2025-03-29","cited_by_count":5,"area":null},{"id":"2111.02679","title":"MixSiam: A Mixture-based Approach to Self-supervised Representation Learning","created":"2021-11-04","cited_by_count":4,"area":null},{"id":"2208.03923","title":"Adversarial robustness of VAEs through the lens of local geometry","created":"2022-08-08","cited_by_count":4,"area":null},{"id":"2303.06032","title":"Exploring Adversarial Attacks on Neural Networks: An Explainable Approach","created":"2023-03-08","cited_by_count":4,"area":null},{"id":"2105.15157","title":"Adaptive Feature Alignment for Adversarial Training","created":"2021-05-31","cited_by_count":3,"area":null},{"id":"2307.11672","title":"Robust Feature Inference: A Test-time Defense Strategy using Spectral Projections","created":"2023-07-21","cited_by_count":3,"area":null},{"id":"2204.10695","title":"Universum-inspired Supervised Contrastive Learning","created":"2022-04-22","cited_by_count":2,"area":null},{"id":"2205.10457","title":"Robust Sensible Adversarial Learning of Deep Neural Networks for Image Classification","created":"2022-05-20","cited_by_count":2,"area":null},{"id":"2211.01579","title":"Data-free Defense of Black Box Models Against Adversarial Attacks","created":"2022-11-03","cited_by_count":2,"area":null},{"id":"2303.06151","title":"NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attacks","created":"2023-03-09","cited_by_count":2,"area":null},{"id":"2311.15165","title":"Mixing Classifiers to Alleviate the Accuracy-Robustness Trade-Off","created":"2023-11-26","cited_by_count":2,"area":null},{"id":"2207.10290","title":"AugRmixAT: A Data Processing and Training Method for Improving Multiple Robustness and Generalization Performance","created":"2022-07-21","cited_by_count":1,"area":null},{"id":"2307.00039","title":"Towards Brain Inspired Design for Addressing the Shortcomings of ANNs","created":"2023-06-30","cited_by_count":1,"area":null},{"id":"2510.13361","title":"Generalist++: A Meta-learning Framework for Mitigating Trade-off in Adversarial Training","created":"2025-10-15","cited_by_count":1,"area":null},{"id":"2103.04565","title":"Improving Transformation-based Defenses against Adversarial Examples with First-order Perturbations","created":"2021-03-08","cited_by_count":0,"area":null},{"id":"2209.02406","title":"Unrestricted Adversarial Samples Based on Non-semantic Feature Clusters Substitution","created":"2022-08-31","cited_by_count":0,"area":null},{"id":"2306.15221","title":"[Re] Double Sampling Randomized Smoothing","created":"2023-06-27","cited_by_count":0,"area":null},{"id":"2308.03236","title":"G-Mix: A Generalized Mixup Learning Framework Towards Flat Minima","created":"2023-08-07","cited_by_count":0,"area":null},{"id":"2310.00183","title":"On the Equivalence of Graph Convolution and Mixup","created":"2023-09-29","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":"2501.01025","title":"Towards Adversarially Robust Deep Metric Learning","created":"2025-01-02","cited_by_count":0,"area":null},{"id":"2602.11204","title":"Zero-Sacrifice Persistent-Robustness Adversarial Defense for Pre-Trained Encoders","created":"2026-02-10","cited_by_count":0,"area":null}],"next_cursor":null}