What is recorded here?
Randomly bypassed residual layers with identity connections during training while using the full-depth network at test time, reducing expected training depth and studying regularization.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Inherits residual/highway connections, dropout and ensemble ideas. Shorter expected paths do not guarantee faster every implementation or equal arbitrary-depth optimization. Reported1200-layer CIFAR results are bounded experiments.
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People and systems
Gao Huang; Yu Sun; Zhuang Liu; Daniel Sedra; Kilian Weinberger
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
Stochastic depth: random residual-path bypass during training
Randomly bypassed residual layers with identity connections during training while using the full-depth network at test time, reducing expected training depth and studying regularization.
Scientific scope
Inherits residual/highway connections, dropout and ensemble ideas. Shorter expected paths do not guarantee faster every implementation or equal arbitrary-depth optimization. Reported1200-layer CIFAR results are bounded experiments.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Gao Huang; Yu Sun; Zhuang Liu; Daniel Sedra; Kilian Weinberger
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.