Robust Neural Network

Robust neural networks aim to create artificial neural networks that are resilient to noisy inputs, adversarial attacks, and variations in data distribution, ensuring reliable performance in real-world applications. Current research focuses on improving certified robustness through techniques like Gaussian loss smoothing, set-based training, and refined adversarial training methods, often applied to architectures such as ResNets and employing algorithms like IBP and PGD. These advancements are crucial for deploying neural networks in safety-critical domains like autonomous driving and medical diagnosis, where reliability and trustworthiness are paramount.