The Effective Depth Paradox: Evaluating the Relationship between Architectural Topology and Trainability in Deep CNNs

1. 1Evaluate the VGG, ResNet, and GoogLeNet architectural families under a unified experimental framework
2. 2Isolate the effects of depth from confounding implementation variables using upscaled CIFAR-10 data
3. 3Introduce a formal distinction between nominal depth and effective depth to better understand trainability
4. 4Analyze the results to identify the effective depth paradox and its implications for CNN design

AI engineers and ML researchers benefit from understanding the relationship between architectural topology and trainability in deep CNNs, as it informs design decisions for image recognition models

💡 The effective depth of a CNN, rather than its nominal depth, is a key factor in determining trainability

💡 Effective depth paradox in CNNs: deeper models aren't always more trainable