Abstract
Benefitted from its great success on many tasks, deep learning is
increasingly used on low-computational-cost devices, e.g. smartphone, embedded
devices, etc. To reduce the high computational and memory cost, in this work,
we propose a fully learnable group convolution module (FLGC for short) which is
quite efficient and can be embedded into any deep neural networks for
acceleration. Specifically, our proposed method automatically learns the group
structure in the training stage in a fully end-to-end manner, leading to a
better structure than the existing pre-defined, two-steps, or iterative
strategies. Moreover, our method can be further combined with depthwise
separable convolution, resulting in 5 times acceleration than the vanilla
Resnet50 on single CPU. An additional advantage is that in our FLGC the number
of groups can be set as any value, but not necessarily 2^k as in most existing
methods, meaning better tradeoff between accuracy and speed. As evaluated in
our experiments, our method achieves better performance than existing learnable
group convolution and standard group convolution when using the same number of
groups.
Description
Fully Learnable Group Convolution for Acceleration of Deep Neural Networks
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