The Effective Depth Paradox: Topology and Trainability in Deep CNNs
The study distinguishes between nominal depth (physical layer count) and effective depth (operational transformation count) in CNNs. It shows that VGG networks saturate early, while architectures like ResNet and GoogLeNet effectively leverage increased depth by decoupling these metrics. This framework helps predict scaling potential and identifies why certain network designs are more trainable than others.