Better Robustness

Improving the robustness of machine learning models, particularly deep neural networks, is a central research focus, aiming to enhance their reliability and performance under various conditions, including adversarial attacks and data distribution shifts. Current efforts concentrate on developing training techniques like adversarial training and multi-norm methods, exploring model architectures such as Vision Transformers and Capsule Networks, and leveraging techniques like data augmentation and ensemble methods to improve generalization and resilience. These advancements are crucial for deploying reliable AI systems in safety-critical applications and for advancing our fundamental understanding of model behavior and generalization.