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Title: LeanResNet: A Low-cost Yet Effective Convolutional Residual Networks
Convolutional Neural Networks (CNNs) filter the input data using spatial convolution operators with compact stencils. Commonly, the convolution operators couple features from all channels, which leads to immense computational cost in the training of and prediction with CNNs. To improve the efficiency of CNNs, we introduce lean convolution operators that reduce the number of parameters and computational complexity, and can be used in a wide range of existing CNNs. Here, we exemplify their use in residual networks (ResNets), which have been very reliable for a few years now and analyzed intensively. In our experiments on three image classification problems, the proposed LeanResNet yields results that are comparable to other recently proposed reduced architectures using similar number of parameters.
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Award ID(s):
1751636 1522599
Publication Date:
Journal Name:
Joint Workshop on On-Device Machine Learning & Compact Deep Neural Network Representations (ODML-CDNNR) at ICML 2019
Sponsoring Org:
National Science Foundation
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