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Recent advances in adversarial training for adversarial robustness
Adversarial training is one of the most effective approaches defending against adversarial
examples for deep learning models. Unlike other defense strategies, adversarial training …
examples for deep learning models. Unlike other defense strategies, adversarial training …
[HTML][HTML] Adversarial training methods for deep learning: A systematic review
Deep neural networks are exposed to the risk of adversarial attacks via the fast gradient sign
method (FGSM), projected gradient descent (PGD) attacks, and other attack algorithms …
method (FGSM), projected gradient descent (PGD) attacks, and other attack algorithms …
Cross-entropy loss functions: Theoretical analysis and applications
Cross-entropy is a widely used loss function in applications. It coincides with the logistic loss
applied to the outputs of a neural network, when the softmax is used. But, what guarantees …
applied to the outputs of a neural network, when the softmax is used. But, what guarantees …
Better diffusion models further improve adversarial training
It has been recognized that the data generated by the denoising diffusion probabilistic
model (DDPM) improves adversarial training. After two years of rapid development in …
model (DDPM) improves adversarial training. After two years of rapid development in …
Las-at: adversarial training with learnable attack strategy
Adversarial training (AT) is always formulated as a minimax problem, of which the
performance depends on the inner optimization that involves the generation of adversarial …
performance depends on the inner optimization that involves the generation of adversarial …
Robustbench: a standardized adversarial robustness benchmark
As a research community, we are still lacking a systematic understanding of the progress on
adversarial robustness which often makes it hard to identify the most promising ideas in …
adversarial robustness which often makes it hard to identify the most promising ideas in …
Cfa: Class-wise calibrated fair adversarial training
Adversarial training has been widely acknowledged as the most effective method to improve
the adversarial robustness against adversarial examples for Deep Neural Networks (DNNs) …
the adversarial robustness against adversarial examples for Deep Neural Networks (DNNs) …
On the robustness of vision transformers to adversarial examples
Recent advances in attention-based networks have shown that Vision Transformers can
achieve state-of-the-art or near state-of-the-art results on many image classification tasks …
achieve state-of-the-art or near state-of-the-art results on many image classification tasks …
Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology
A model's ability to express its own predictive uncertainty is an essential attribute for
maintaining clinical user confidence as computational biomarkers are deployed into real …
maintaining clinical user confidence as computational biomarkers are deployed into real …
A closer look at accuracy vs. robustness
Current methods for training robust networks lead to a drop in test accuracy, which has led
prior works to posit that a robustness-accuracy tradeoff may be inevitable in deep learning …
prior works to posit that a robustness-accuracy tradeoff may be inevitable in deep learning …