{"total":58,"rows":[{"id":"2308.03177","title":"Boosting Few-shot 3D Point Cloud Segmentation via Query-Guided Enhancement","created":"2023-08-06","cited_by_count":19,"area":null},{"id":"2401.11824","title":"Rethinking Centered Kernel Alignment in Knowledge Distillation","created":"2024-01-22","cited_by_count":17,"area":"distill"},{"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":"2401.16352","title":"Adversarial Training on Purification (AToP): Advancing Both Robustness and Generalization","created":"2024-01-29","cited_by_count":15,"area":null},{"id":"2503.01811","title":"AutoAdvExBench: Benchmarking autonomous exploitation of adversarial example defenses","created":"2025-03-03","cited_by_count":15,"area":null},{"id":"2408.05446","title":"Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness","created":"2024-08-08","cited_by_count":12,"area":null},{"id":"2410.12307","title":"DAT: Improving Adversarial Robustness via Generative Amplitude Mix-up in Frequency Domain","created":"2024-10-16","cited_by_count":12,"area":null},{"id":"2402.18133","title":"Classes Are Not Equal: An Empirical Study on Image Recognition Fairness","created":"2024-02-28","cited_by_count":10,"area":null},{"id":"2406.18451","title":"Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers","created":"2024-06-26","cited_by_count":10,"area":null},{"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,"area":null},{"id":"2403.16067","title":"Adversarial Guided Diffusion Models for Adversarial Purification","created":"2024-03-24","cited_by_count":8,"area":"diffusion"},{"id":"2309.02752","title":"SWAP: Exploiting Second-Ranked Logits for Adversarial Attacks on Time Series","created":"2023-09-06","cited_by_count":6,"area":null},{"id":"2310.04285","title":"Assessing Robustness via Score-Based Adversarial Image Generation","created":"2023-10-06","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":"2503.08038","title":"Generalized Kullback-Leibler Divergence Loss","created":"2025-03-11","cited_by_count":6,"area":"distill"},{"id":"2604.00223","title":"Diversity-Aware Reverse Kullback-Leibler Divergence for Large Language Model Distillation","created":"2026-03-31","cited_by_count":5,"area":"distill"},{"id":"2310.12793","title":"OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift","created":"2023-10-19","cited_by_count":4,"area":null},{"id":"2405.05075","title":"Sparse-PGD: A Unified Framework for Sparse Adversarial Perturbations Generation","created":"2024-05-08","cited_by_count":4,"area":null},{"id":"2505.23161","title":"Implicit Inversion turns CLIP into a Decoder","created":"2025-05-29","cited_by_count":4,"area":null},{"id":"2402.07183","title":"A Random Ensemble of Encrypted Vision Transformers for Adversarially Robust Defense","created":"2024-02-11","cited_by_count":3,"area":null},{"id":"2406.19622","title":"Data-Driven Lipschitz Continuity: A Cost-Effective Approach to Improve Adversarial Robustness","created":"2024-06-28","cited_by_count":3,"area":null},{"id":"2411.15210","title":"Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks","created":"2024-11-20","cited_by_count":3,"area":null},{"id":"2410.21952","title":"On the Robustness of Adversarial Training Against Uncertainty Attacks","created":"2024-10-29","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":"2508.13309","title":"DASH: A Meta-Attack Framework for Synthesizing Effective and Stealthy Adversarial Examples","created":"2025-08-18","cited_by_count":2,"area":null},{"id":"2508.17680","title":"Robustness Feature Adapter for Efficient Adversarial Training","created":"2025-08-25","cited_by_count":2,"area":null},{"id":"2308.04137","title":"A Comprehensive Assessment Benchmark for Rigorously Evaluating Deep Learning Image Classifiers","created":"2023-08-08","cited_by_count":1,"area":null},{"id":"2406.03143","title":"ZeroPur: Succinct Training-Free Adversarial Purification","created":"2024-06-05","cited_by_count":1,"area":null},{"id":"2407.02240","title":"MALT Powers Up Adversarial Attacks","created":"2024-07-02","cited_by_count":1,"area":null},{"id":"2408.05952","title":"Optimizing Vision Transformers with Data-Free Knowledge Transfer","created":"2024-08-12","cited_by_count":1,"area":"distill"},{"id":"2410.16805","title":"Test-time Adversarial Defense with Opposite Adversarial Path and High Attack Time Cost","created":"2024-10-22","cited_by_count":1,"area":null},{"id":"2502.09352","title":"Wasserstein distributional adversarial training for deep neural networks","created":"2025-02-13","cited_by_count":1,"area":null},{"id":"2504.03782","title":"Deep Positive-Negative Prototypes for Adversarially Robust Discriminative Prototypical Learning","created":"2025-04-03","cited_by_count":1,"area":null},{"id":"2507.14503","title":"Generative Distribution Distillation","created":"2025-07-19","cited_by_count":1,"area":"distill"},{"id":"2509.20989","title":"Rejuvenating Cross-Entropy Loss in Knowledge Distillation for Recommender Systems","created":"2025-09-25","cited_by_count":1,"area":"distill"},{"id":"2512.12997","title":"Calibrating Uncertainty for Zero-Shot Adversarial CLIP","created":"2025-12-15","cited_by_count":1,"area":null},{"id":"2404.06313","title":"On adversarial training and the 1 Nearest Neighbor classifier","created":"2024-04-09","cited_by_count":0,"area":null},{"id":"2408.03972","title":"Enhancing Output Diversity Improves Conjugate Gradient-based Adversarial Attacks","created":"2024-08-07","cited_by_count":0,"area":null},{"id":"2410.18622","title":"Environment Maps Editing using Inverse Rendering and Adversarial Implicit Functions","created":"2024-10-24","cited_by_count":0,"area":null},{"id":"2412.15983","title":"Never Reset Again: A Mathematical Framework for Continual Inference in Recurrent Neural Networks","created":"2024-12-20","cited_by_count":0,"area":null},{"id":"2501.16904","title":"Diffusion or Non-Diffusion Adversarial Defenses: Rethinking the Relation between Classifier and Adversarial Purifier","created":"2025-01-28","cited_by_count":0,"area":"diffusion"},{"id":"2502.17972","title":"Model-Free Adversarial Purification via Coarse-To-Fine Tensor Network Representation","created":"2025-02-25","cited_by_count":0,"area":null},{"id":"2502.21041","title":"Fast Adversarial Training against Sparse Attacks Requires Loss Smoothing","created":"2025-02-28","cited_by_count":0,"area":null},{"id":"2503.20583","title":"Feature Statistics with Uncertainty Help Adversarial Robustness","created":"2025-03-26","cited_by_count":0,"area":null},{"id":"2510.05317","title":"RegMix: Adversarial Mutual and Generalization Regularization for Enhancing DNN Robustness","created":"2025-10-06","cited_by_count":0,"area":null},{"id":"2510.10000","title":"Tight Robustness Certificates and Wasserstein Distributional Attacks for Deep Neural Networks","created":"2025-10-11","cited_by_count":0,"area":null},{"id":"2512.10275","title":"Sample-wise Adaptive Weighting for Transfer Consistency in Adversarial Distillation","created":"2025-12-11","cited_by_count":0,"area":null},{"id":"2601.15657","title":"Integrating Knowledge Distillation Methods: A Sequential Multi-Stage Framework","created":"2026-01-22","cited_by_count":0,"area":"distill"},{"id":"2602.19931","title":"Expanding the Role of Diffusion Models for Robust Classifier Training","created":"2026-02-23","cited_by_count":0,"area":"diffusion"},{"id":"2603.23860","title":"Why the Maximum Second Derivative of Activations Matters for Adversarial Robustness","created":"2026-03-25","cited_by_count":0,"area":null},{"id":"2604.06954","title":"Compression as an Adversarial Amplifier Through Decision Space Reduction","created":"2026-04-08","cited_by_count":0,"area":null},{"id":"2604.11590","title":"Learning Robustness at Test-Time from a Non-Robust Teacher","created":"2026-04-13","cited_by_count":0,"area":null},{"id":"2605.08464","title":"The Geometric Structure of Models Learning Sparse Data","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2605.23183","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","created":"2026-05-22","cited_by_count":0,"area":"moe"},{"id":"2606.01746","title":"Sensitivity as a Double-Edged Sword: A Trade-off Between Discriminability and Adversarial Robustness","created":"2026-06-01","cited_by_count":0,"area":null},{"id":"2606.27696","title":"Class-frequency Guided Noise Schedule for Diffusion Models","created":"2026-06-26","cited_by_count":0,"area":"diffusion"},{"id":"2606.31653","title":"Improving Certified Robustness via Adversarial Distillation","created":"2026-06-30","cited_by_count":0,"area":null},{"id":"2607.19855","title":"Adversarial Frontiers: Minimum-Norm Attack Ensembles for Robustness Evaluation","created":"2026-07-22","cited_by_count":0,"area":null}],"next_cursor":null}