Abstract
State-of-the-art deep neural networks (DNNs) have greatly improved the performance of facial landmarks detection. However, DNN models usually have a large number of parameters, which leads to high computational complexity and memory cost. To address this problem, we propose a method to compress large deep neural networks, which includes three steps. (1) Importance-based neuron pruning: compared with traditional connection pruning, we introduce weights correlations to prune unimportant neurons, which can reduce index storage and inference computation costs. (2) Product quantization: further use of product quantization helps to enforce weights sharing, which stores fewer cluster indexes and codebooks than scalar quantization. (3) Network retraining: to reduce training difficulty and performance degradation, we iteratively retrain the network, compressing one layer at a time. Experiments of compressing a VGG-like model for facial landmarks detection demonstrate that the proposed method achieves 26x compression of the model with 1.5% performance degradation.
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Zeng, D., Zhao, F., Bao, Y. (2016). Compressing Deep Neural Network for Facial Landmarks Detection. In: Liu, CL., Hussain, A., Luo, B., Tan, K., Zeng, Y., Zhang, Z. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2016. Lecture Notes in Computer Science(), vol 10023. Springer, Cham. https://doi.org/10.1007/978-3-319-49685-6_10
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