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From Deep to Shallow: Unconstrained and Efficient Layer Merging Strategy

arXiv · AI, language, vision and robotics · article · Sep 4, 2026 · UTC

Although Deep Neural Networks have become foundational in many areas of Machine Learning, high computational demands limit their application in resource-constrained environments. To address this issue, depth compression methods have been proposed to identify and linearize redundant activation functions, thereby allowing for the merging of layers without intermediate non-linearities. However, these methods face two key challenges: they cannot be directly applied to convolutions with padding due to the absence of an analytical solution for merging these layers, and they typically increase the ke

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