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Adversarial Learning

4,854 papers

Papers per year

Papers

Center-Aware Adversarial Augmentation for Single Domain Generalization WACV 2023
FLOAT: Fast Learnable Once-for-All Adversarial Training for Tunable Trade-Off Between Accuracy and Robustness WACV 2023
Adversarial Robustness in Discontinuous Spaces via Alternating Sampling & Descent WACV 2023
Backprop Induced Feature Weighting for Adversarial Domain Adaptation With Iterative Label Distribution Alignment WACV 2023
Explainability-Aware One Point Attack for Point Cloud Neural Networks WACV 2023
Treatment Learning Causal Transformer for Noisy Image Classification WACV 2023
Robustness of Trajectory Prediction Models Under Map-Based Attacks WACV 2023
DE-CROP: Data-Efficient Certified Robustness for Pretrained Classifiers WACV 2023
FFM: Injecting Out-of-Domain Knowledge via Factorized Frequency Modification WACV 2023
PatchZero: Defending Against Adversarial Patch Attacks by Detecting and Zeroing the Patch WACV 2023
Interpreting Disparate Privacy-Utility Tradeoff in Adversarial Learning via Attribute Correlation WACV 2023
Closer Look at the Transferability of Adversarial Examples: How They Fool Different Models Differently WACV 2023
Avoiding Lingering in Learning Active Recognition by Adversarial Disturbance WACV 2023
Do Adaptive Active Attacks Pose Greater Risk Than Static Attacks? WACV 2023
Improving Diversity With Adversarially Learned Transformations for Domain Generalization WACV 2023
Adversarial Local Distribution Regularization for Knowledge Distillation WACV 2023
Self-Supervised Monocular Depth Estimation From Thermal Images via Adversarial Multi-Spectral Adaptation WACV 2023
Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency Domain NIPS 2022
Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation NIPS 2022
Robust Binary Models by Pruning Randomly-initialized Networks NIPS 2022
Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model NIPS 2022
Why Do Artificially Generated Data Help Adversarial Robustness NIPS 2022
Pre-trained Adversarial Perturbations NIPS 2022
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness NIPS 2022
Efficient and Effective Augmentation Strategy for Adversarial Training NIPS 2022