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Life sciences · Preprint
arXiv · September 4, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is a preprint presenting a novel neural network layer-merging strategy that claims to overcome prior limitations (handling padded convolutions, avoiding kernel size expansion) and reports inference speed-up on embedded platforms. The work is methodological and unreviewed; it demonstrates proof of concept but does not report quantitative effect sizes, comparisons to existing compression methods, or statistical significance testing.
Preprint. Intervention: Layer merging strategy for neural network depth compression without increasing kernel size and applicable to padded convolutions..
Proposed method enables merging of layers without analytical solutions and without increasing kernel size, addressing two limitations of prior depth compression methods. Validation reported across multiple architectures and datasets with inference speed-up measured on real embedded platforms; specific speed-up magnitudes not stated in abstract.
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A novel computational method for neural network compression with proof-of-concept validation across architectures, but lacking peer review, clinical application, or comparison to established baselines.
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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 kernel size of merged layers, thus limiting speed-up gains. To overcome these limitations, we propose an efficient strategy that enables merging of layers without an existing analytical solution, and also without increasing kernel size. We validate our approach across multiple architectures and datasets, and measure inference speed-up gains on real embedded platforms. We publicly released the code at https://github.com/ShulzhenkoPetr/deep-to-shallow.
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