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arXiv · 2312.09922

A Unifying Tensor View for Lightweight CNNs

Jason Chun Lok Li·Rui Lin·Jiajun Zhou·Edmund Yin Mun Lam·Ngai Wong
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Abstract

Despite the decomposition of convolutional kernels for lightweight CNNs being well studied, existing works that rely on tensor network diagrams or hyperdimensional abstraction lack geometry intuition. This work devises a new perspective by linking a 3D-reshaped kernel tensor to its various slice-wise and rank-1 decompositions, permitting a straightforward connection between various tensor approximations and efficient CNN modules. Specifically, it is discovered that a pointwise-depthwise-pointwise (PDP) configuration constitutes a viable construct for lightweight CNNs. Moreover, a novel link to the latest ShiftNet is established, inspiring a first-ever shift layer pruning that achieves nearly 50% compression with < 1% drop in accuracy for ShiftResNet.

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Jason Chun Lok Li, Rui Lin, Jiajun Zhou, Edmund Yin Mun Lam, Ngai Wong. 2023-12-15. A Unifying Tensor View for Lightweight CNNs. https://arxiv.org/abs/2312.09922

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