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Compressing and Accelerating Neural Network for Facial Point Localization


State-of-the-art deep neural networks (DNNs) have greatly improved the accuracy of facial landmark localization. However, DNN models usually have a huge number of parameters which cause high memory cost and computational complexity. To address this issue, a novel method is proposed to compress and accelerate large DNN models while maintaining the performance. It includes three steps: (1) importance-based pruning: compared with traditional connection pruning, weight correlations are introduced to find and prune unimportant neurons or connections. (2) Product quantization: product quantization helps to enforce weights shared. With the same size codebook, product quantization can achieve higher compression rate than scalar quantization. (3) Network retraining: to reduce compression difficulty and performance degradation, the network is retrained iteratively after compressing one layer at a time. Besides, all pooling layers are removed and the strides of their neighbor convolutional layers are increased to accelerate the network simultaneously. The experimental results of compressing a VGG-like model demonstrate the effectiveness of our proposed method, which achieves 26 × compression and 4 × acceleration while the root mean squared error (RMSE) increases by just 3.6%.

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We would like to thank professor Tian Qi (the University of Texas at San Antonio) and Zhou Xi (Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences) for their suggestions to this project.


This work is supported by the National Natural Science Foundation of China (61572307).

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Correspondence to Dan Zeng.

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Zeng, D., Zhao, F., Shen, W. et al. Compressing and Accelerating Neural Network for Facial Point Localization. Cogn Comput 10, 359–367 (2018).

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  • Network compression
  • Network acceleration
  • Pruning
  • Product quantization
  • Facial point localization