Regularized Training Framework for Combining Pruning and Quantization to Compress Neural Networks
| Autor: | Daosen Zhai, Ding Qimin, Ruonan Zhang, Yi Jiang, Bin Li |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Contextual image classification
Artificial neural network business.industry Computer science Computation Quantization (signal processing) 020207 software engineering 02 engineering and technology Image segmentation 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Convolutional neural network 0202 electrical engineering electronic engineering information engineering Artificial intelligence business computer 0105 earth and related environmental sciences |
| Zdroj: | WCSP |
| DOI: | 10.1109/wcsp.2019.8928083 |
| Popis: | Many convolutional neural networks(CNNs) have been proposed to solve computer vision tasks such as image classification and image segmentation. However the CNNs usually contain a large number of parameters to determine which consumes very high computation and power resources. Thus, it is difficult to deploy the CNNs on resource-limited devices. Network pruning and network quantization are two main methods to compress the CNNs, researchers often apply these methods individually without considering the relationship between them. In this paper, we explore the coupling relationship between network pruning and quantization, as well as the limits of the current network compression training method. Then we propose a new regularized training method that can combine pruning and quantization within a simple training framework. Experiments show that by using the proposed training framework, the finetune process is not needed anymore and hence we can reduce much time for training a network. The simulation results also show that the performance of the network can over-perform the traditional methods. The proposed framework is suitable for the CNNs deployed in portable devices with limited computational resources and power supply. |
| Databáze: | OpenAIRE |
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