adversarial robustness
Explicit Tradeoffs between Adversarial and Natural Distributional Robustness
Results averaged over ResNet18 and ResNet50.
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DAT: Improving Adversarial Robustness via Generative Amplitude Mix-up in Frequency Domain Fengpeng Li1 Kemou Li1 Haiwei Wu
Recent studies show that adversarial attacks disproportionately impact the patterns within the phase of the sample's frequency spectrum--typically containing
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CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense
CAS stands for Chinese Academy of Sciences 38th Conference on Neural Information Processing Systems (NeurIPS 2024).
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Improving Robustness with Adaptive Weight Decay
Deep Neural Networks (DNNs) have exceeded human capability on many computer vision tasks.
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Characterization of Overfitting in Robust Multiclass Classification
Nonetheless, modern machine learning is adaptive in its nature. Prior information about a model's performance on the test set inevitably influences
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