Wide Resnets

Wide residual networks (ResNets) are a class of deep learning models designed to overcome challenges in training extremely deep neural networks. Current research focuses on understanding their training dynamics, particularly through mean-field analysis and the impact of scaling factors on generalization ability, as well as exploring architectural modifications like incorporating transfer learning and wavelet regularization to improve performance and robustness against adversarial attacks. These investigations aim to enhance the theoretical understanding and practical application of wide ResNets in various domains, leading to more efficient and reliable deep learning models.