Improved Residual Networks for Image and Video Recognition

Understand

Residual networks (ResNets) represent a powerful type of convolutional neural network (CNN) architecture, widely adopted and used in various tasks.

• In this work we propose an improved version of ResNets.
• Our proposed improvements address all three main components of a ResNet: the flow of information through the network layers, the residual building block, and the projection shortcut.
• We are able to show consistent improvements in accuracy and learning convergence over the baseline.