How can deep learning architectures be designed to improve generalization under limited or noisy training data while maintaining robustness and interpretability?

 

Deep learning models have achieved remarkable performance across domains such as computer vision, natural language processing, and scientific modeling. However, challenges remain in areas including generalization beyond training distributions, interpretability of learned representations, and robustness to noisy or limited datasets. 

I am particularly interested in understanding which architectural innovations, training strategies, or theoretical insights have shown the most promise in improving generalization and robustness while maintaining computational efficiency. Insights from recent research or practical experiences with large-scale models would be especially valuable.