ResNet Model

ResNet, or Residual Network, models are a class of deep neural networks designed to overcome the vanishing gradient problem in very deep architectures, enabling the training of significantly deeper networks than previously possible through the use of skip connections. Current research focuses on improving ResNet's efficiency, interpretability, and robustness through techniques like dynamic weight adjustment, novel regularization methods (e.g., 1-path-norm), and exploring alternative architectures such as hyperbolic ResNets and those incorporating attention mechanisms. These advancements have broad implications across various fields, including image classification, speech recognition, and signal processing, leading to improved accuracy and efficiency in numerous applications.