Residual Block

Residual blocks are fundamental building blocks in deep neural networks, designed to mitigate the vanishing gradient problem and enable the training of significantly deeper architectures. Current research focuses on optimizing residual block designs within various network architectures, including U-Nets, ResNets, and transformers, often incorporating techniques like attention mechanisms, dynamic filtering, and multi-scale feature extraction to improve performance in diverse applications. These improvements lead to enhanced accuracy and efficiency in image processing tasks such as segmentation, super-resolution, and dehazing, as well as applications in medical image analysis, autonomous driving, and other fields requiring efficient and robust deep learning models. The ongoing refinement of residual blocks is crucial for advancing the capabilities and applicability of deep learning across numerous domains.