Lst-net: Learning a convolutional neural network with a learnable sparse transform

Lida Li, Kun Wang, Shuai Li, Xiangchu Feng, Lei Zhang

Abstract: The 2D convolutional (Conv2d) layer is the fundamental element to a deep convolutional neural network (CNN). Despite the great success of CNN, the conventional Conv2d is still limited in effectively reducing the spatial and channel-wise redundancy of features. In this paper, we propose to mitigate this issue by learning a CNN with a learnable sparse transform (LST), which converts the input features into a more compact and sparser domain so that the spatial and channel-wise redundancy can be more effectively reduced. The proposed LST can be efficiently implemented with existing CNN modules, such as point-wise and depth-wise separable convolutions, and it is portable to existing CNN architectures for seamless training and inference.