AI RESEARCH
The Effective Depth Paradox: Evaluating the Relationship between Architectural Topology and Trainability in Deep CNNs
arXiv CS.AI
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ArXi:2602.13298v2 Announce Type: replace-cross This paper investigates the relationship between convolutional neural network (CNN) and image recognition performance through a comparative study of the VGG, ResNet and GoogLeNet architectural families. By evaluating these models under a unified experimental framework on upscaled CIFAR-10 data, we isolate the effects of depth from confounding implementation variables. We