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Oral
LegoNet: Efficient Convolutional Neural Networks with Lego Filters
Zhaohui Yang · Yunhe Wang · Chuanjian Liu · Hanting Chen · Chunjing Xu · Boxin Shi · Chao Xu · Chang Xu

Wed Jun 12 02:30 PM -- 02:35 PM (PDT) @ Hall A
in Deep Learning Architectures »

This paper aims to build efficient convolutional neural networks using a set of Lego filters. Many successful building blocks, e.g., inception and residual modules, have been designed to refresh state-of-the-art records of CNNs on visual recognition tasks. Beyond these high-level modules, we suggest that an ordinary filter in the neural network can be upgraded to a sophisticated module as well. Filter modules are established by assembling a shared set of Lego filters that are often of much lower dimensions. Weights in Lego filters and binary masks to stack Lego filters for these filter modules can be simultaneously optimized in an end-to-end manner as usual. Inspired by network engineering, we develop a split-transform-merge strategy for an efficient convolution by exploiting intermediate Lego feature maps. The compression and acceleration achieved by Lego Networks using the proposed Lego filters have been theoretically discussed. Experimental results on benchmark datasets and deep models demonstrate the advantages of the proposed Lego filters and their potential real-world applications on mobile devices.

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Author Information

Zhaohui Yang (Peking University)
Yunhe Wang (Peking University)
Chuanjian Liu (Huawei Noah's Ark Lab)
Hanting Chen (Peking University)
Chunjing Xu (Huawei Noah's Ark Lab)
Boxin Shi (Peking University)
Chao Xu (Peking University)
Chang Xu (University of Sydney)

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