Enhancing Neural Networks with Channel Gating

This chapter explores innovative methods for improving neural network performance through channel gating, focusing on fine-grained gating of individual filters. It discusses various strategies for multitask and continual learning while introducing advanced techniques like batch shaping loss and beta-like distributions for gating mechanisms. The authors also analyze the adjustments made to ResNet-18 to optimize computational efficiency without sacrificing performance, comparing its complexity to that of ResNet-50.