IGCV$2$: Interleaved Structured Sparse Convolutional Neural Networks

17 Apr 2018 • Guotian Xie • Jingdong Wang • Ting Zhang • Jian-Huang Lai • Richang Hong • Guo-Jun Qi

In this paper, we study the problem of designing efficient convolutional neural network architectures with the interest in eliminating the redundancy in convolution kernels. In addition to structured sparse kernels, low-rank kernels and the product of low-rank kernels, the product of structured sparse kernels, which is a framework for interpreting the recently-developed interleaved group convolutions (IGC) and its variants (e.g., Xception), has been attracting increasing interests... (read more)

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METHOD	TYPE
Depthwise Convolution	Convolutions
Pointwise Convolution	Convolutions
Residual Connection	Skip Connections
Convolution	Convolutions
Average Pooling	Pooling Operations
Global Average Pooling	Pooling Operations
Depthwise Separable Convolution	Convolutions
Max Pooling	Pooling Operations
Softmax	Output Functions
1x1 Convolution	Convolutions
ReLU	Activation Functions
Dense Connections	Feedforward Networks
Xception	Convolutional Neural Networks

METHOD

TYPE

Depthwise Convolution