Effective depth, an operational count of sequential transformations, predicts CNN trainability better than nominal layer count because shortcuts and branches decouple the two.
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The Effective Depth Paradox: Evaluating the Relationship between Architectural Topology and Trainability in Deep CNNs
Effective depth, an operational count of sequential transformations, predicts CNN trainability better than nominal layer count because shortcuts and branches decouple the two.