Improved Residual Network based on norm-preservation for visual recognition

This paper proposes architectural refinements to ResNet that address the information flow through several layers of the network, including the input stem, downsampling block, projection shortcut, and identity blocks, which enhance the learning dynamics, resulting in high accuracy and inference performance by enforcing norm-preservation throughout the network training.