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About this paper
Klaus Greff, Rupesh K. Srivastava, Jürgen Schmidhuber
A new conceptual framework reinterprets Highway and Residual Networks, proposing that layers within a stage iteratively refine the same feature representation rather than compute new abstractions. This framework unifies the mathematical formulations of both architectures and explains their robustness to layer removal and shuffling, providing empirical evidence from ImageNet and language modeling tasks.
Key insights
- Both Highway and Residual Networks can be derived from the principle of "unrolled iterative estimation," where layers within a stage iteratively refine an existing feature representation rather than generating entirely new ones.
- This iterative refinement explains previously puzzling empirical observations, such as the resilience of these networks to layer removal, their tolerance to layer shuffling, and the sparse transform gate activity observed in Highway Networks.
- The
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