Robust Network

Robust network research focuses on designing and training neural networks that are resilient to various forms of noise, adversarial attacks, and data variations, ensuring reliable performance in real-world applications. Current efforts concentrate on improving training methods (e.g., incorporating optimal transport or adversarial training), developing novel architectures (e.g., lightweight networks with attention mechanisms), and analyzing the underlying causes of vulnerability through representation similarity and causal inference. This field is crucial for deploying AI systems in safety-critical domains like autonomous driving and healthcare, where robustness is paramount, and for advancing our fundamental understanding of neural network generalization and stability.