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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?
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Ch.1 Residual Neural Network Fundamentals - Overcoming Neural Network Degradation Through Residual Learning @ University of Michigan - Ann Arbor
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Match each concept regarding the identity mapping paradox to its correct description.
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.
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?