Adversarial Mask

Adversarial masking is a technique used to improve the robustness and generalization of machine learning models, particularly in scenarios with limited data or noisy labels, by strategically obscuring parts of the input data. Current research focuses on developing sophisticated masking strategies, often employing generative adversarial networks (GANs) or other adversarial training methods, to create challenging training samples and enhance model performance. This approach has shown promise in various applications, including face recognition, medical image analysis, and reinforcement learning, by improving model accuracy and resistance to adversarial attacks while also potentially increasing interpretability.