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A deeper architecture should theoretically never yield higher training error than a shallower counterpart because the solution space of the shallower model is a ___ of the deeper model.
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Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
Ch.3 Deep Residual Network Architecture - Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
The Degradation Problem in Deep Networks - Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
Related
Match each concept regarding the identity mapping paradox to its correct description.
Order the steps used to demonstrate the identity mapping construction principle for deep architectures.
Explain why the 56-layer network's higher training error demonstrates an optimization problem rather than a representational limitation, referencing the identity mapping construction.
According to the identity mapping construction proof, how can a deeper architecture be configured to achieve the exact same function and training error as a learned shallower model?
In practice, what empirical problem arises when applying standard gradient-based optimization solvers to plain deep networks, despite the theoretical existence of a solution matching a shallower counterpart?