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3 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Fully Learnable Group Convolution for Acceleration of Deep Neural Networks

   Wang, Kan, Shan and Chen · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2019

   The paper proposes a learnable group convolution module that learns its group structure during training, speeds up deep networks, and performs better than existing group convolution methods with the same number of groups.

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   1. UncheckedCombining the proposed learnable group convolution with depthwise separable convolution gives about 5 times the speed of a standard ResNet50 on a single CPU.“Moreover, our method can be further combined with depthwise separable convolution, resulting in 5 times acceleration than the vanilla Resnet50 on single CPU.”
   2. UncheckedIn the authors' fully learnable group convolution, the number of groups can be any value, not just powers of two, which they say gives a better accuracy-speed tradeoff.“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.”
   3. UncheckedThe authors report that their fully learnable group convolution outperforms existing learnable and standard group convolution when the number of groups is the same.“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.”