Cross-domain structural pattern
Compression reduces data size by removing redundancy. Information loss (distortion) increases with compression ratio. Rate-distortion function defines optimal trade-off boundary. Perceptual metrics weight distortion by human sensitivity patterns. Adaptive quantization allocates bits based on local information content.
view paper→Loss scales inversely with depth due to functionally similar layers reducing error through ensemble averaging rather than compositional learning. Residual architecture bias combined with incompatible target functions produces inefficient yet robust regime. Improving efficiency requires architectural innovations encouraging compositional depth usage instead of redundant layer averaging.
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