Residual Network

Residual networks (ResNets) are deep neural network architectures designed to overcome the vanishing gradient problem during training, enabling the effective training of very deep models. Current research focuses on improving ResNet performance and efficiency through modifications like dynamic weight adjustment, lightweight convolutional modules, and optimized network structures, as well as exploring their applications in diverse fields such as physics simulation, image processing, and biometric authentication. The widespread adoption of ResNets and their variants stems from their ability to achieve state-of-the-art results across numerous tasks while offering avenues for improved efficiency and interpretability.