Robust Deep

Robust deep learning focuses on developing deep neural networks (DNNs) that maintain high accuracy even when faced with noisy data, adversarial attacks, or variations in input distributions. Current research emphasizes improving robustness through techniques like adversarial training, ensemble methods, and biologically-inspired architectures (e.g., incorporating features of the visual cortex), as well as exploring novel activation functions and optimization strategies. This field is crucial for deploying DNNs in safety-critical applications (e.g., autonomous driving, medical diagnosis) and for enhancing the reliability and trustworthiness of AI systems more broadly.