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Found 9 papers, 4 papers with code
Fair Sampling in Diffusion Models through Switching Mechanism
Diffusion models have shown their effectiveness in generation tasks by well-approximating the underlying probability distribution.
Differentially Private Sharpness-Aware Training
Training deep learning models with differential privacy (DP) results in a degradation of performance.
Exploring the Effect of Multi-step Ascent in Sharpness-Aware Minimization
Recently, Sharpness-Aware Minimization (SAM) has shown state-of-the-art performance by seeking flat minima.
Stability Analysis of Sharpness-Aware Minimization
Utilizing the qualitative theory of dynamical systems, we explain how SAM becomes stuck in the saddle point and then theoretically prove that the saddle point can become an attractor under SAM dynamics.
Comment on Transferability and Input Transformation with Additive Noise
Adversarial attacks have verified the existence of the vulnerability of neural networks.
Bridged Adversarial Training
Adversarial robustness is considered as a required property of deep neural networks.
GradDiv: Adversarial Robustness of Randomized Neural Networks via Gradient Diversity Regularization
Our experiments on MNIST, CIFAR10, and STL10 show that our proposed GradDiv regularizations improve the adversarial robustness of randomized neural networks against a variety of state-of-the-art attack methods.
Understanding Catastrophic Overfitting in Single-step Adversarial Training
Although fast adversarial training has demonstrated both robustness and efficiency, the problem of "catastrophic overfitting" has been observed.
Torchattacks: A PyTorch Repository for Adversarial Attacks
Torchattacks is a PyTorch library that contains adversarial attacks to generate adversarial examples and to verify the robustness of deep learning models.
