A Unifying Tensor View for Lightweight CNNs
Jason Chun Lok Li Rui Lin Jiajun Zhou Edmund Yin Mun Lam Ngai Wong
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.