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Found 5 papers, 1 papers with code
A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies
In this paper, we present a comprehensive survey trying to offer a systematic and structured investigation on robust adversarial training in pattern recognition.
Perturbation Diversity Certificates Robust Generalisation
It is possibly due to the fact that the conventional adversarial training methods generate adversarial perturbations usually in a supervised way, so that the adversarial samples are highly biased towards the decision boundary, resulting in an inhomogeneous data distribution.
Improving Model Robustness with Latent Distribution Locally and Globally
The proposed adversarial training with latent distribution (ATLD) method defends against adversarial attacks by crafting LMAEs with the latent manifold in an unsupervised manner.
Robust Generative Adversarial Network
Generative adversarial networks (GANs) are powerful generative models, but usually suffer from instability and generalization problem which may lead to poor generations.
Generative Adversarial Classifier for Handwriting Characters Super-Resolution
Generative Adversarial Networks (GAN) receive great attentions recently due to its excellent performance in image generation, transformation, and super-resolution.
