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arXiv · 1902.10949

Dynamic Multi-path Neural Network

Yingcheng Su·Shunfeng Zhou·Yichao Wu·Tian Su·Ding Liang·Jiaheng Liu·Dixin Zheng·Yingxu Wang·Junjie Yan·Xiaolin Hu
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Abstract

Although deeper and larger neural networks have achieved better performance, the complex network structure and increasing computational cost cannot meet the demands of many resource-constrained applications. Existing methods usually choose to execute or skip an entire specific layer, which can only alter the depth of the network. In this paper, we propose a novel method called Dynamic Multi-path Neural Network (DMNN), which provides more path selection choices in terms of network width and depth during inference. The inference path of the network is determined by a controller, which takes into account both previous state and object category information. The proposed method can be easily incorporated into most modern network architectures. Experimental results on ImageNet and CIFAR-100 demonstrate the superiority of our method on both efficiency and overall classification accuracy. To be specific, DMNN-101 significantly outperforms ResNet-101 with an encouraging 45.1% FLOPs reduction, and DMNN-50 performs comparably to ResNet-101 while saving 42.1% parameters.

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Yingcheng Su, Shunfeng Zhou, Yichao Wu, Tian Su, Ding Liang, Jiaheng Liu, Dixin Zheng, Yingxu Wang, Junjie Yan, Xiaolin Hu. 2019-02-28. Dynamic Multi-path Neural Network. https://arxiv.org/abs/1902.10949

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