The Effective Depth Paradox: Topology and Trainability in Deep CNNs

תקציר מקורי באנגליתarXiv:2602.13298v4 Announce Type: replace-cross Abstract: This paper presents a controlled comparative study of convolutional neural network (CNN) topology and image classification performance across the architectural families VGG, ResNet, and GoogLeNet, evaluated on CIFAR-10 under a unified training protocol. We formalize the distinction between nominal depth ($D_{\mathrm{nom}}$), the physical count of weight-bearing layers, and effective depth ($D_{\mathrm{eff}}$), an operational metric quantifying the expected length of forward information paths, extending the path-ensemble interpretation of residual networks introduced by Veit et al. (2016) into closed-form, pre-training proxies spanning sequential, residual, and multi-branch topologies. We validate this proxy against a gradient-weight