| Paper ID | MLSP-3.2 |
| Paper Title |
EVOLVING QUANTIZED NEURAL NETWORKS FOR IMAGE CLASSIFICATION USING A MULTI-OBJECTIVE GENETIC ALGORITHM |
| Authors |
Yong Wang, Xiaojing Wang, Xiaoyu He, Central South University, China |
| Session | MLSP-3: Deep Learning Training Methods 3 |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 13:00 - 13:45 |
| Presentation Time: | Tuesday, 08 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-DEEP] Deep learning techniques |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Recently, many model quantization approaches have been investigated to reduce the model size and improve the inference speed of convolutional neural networks (CNNs). However, these approaches usually inevitably lead to a decrease in classification accuracy. To address this problem, this paper proposes a mixed precision quantization method combined with channel expansion of CNNs by using a multi-objective genetic algorithm, called MOGAQNN. In MOGAQNN, each individual in the population is used to encode a mixed precision quantization policy and a channel expansion policy. During the evolution process, the two polices are optimized simultaneously by the non-dominated sorting genetic algorithm II (NSGA-II). Finally, we choose the best individual in the last population and evaluate its performance on the test set as the final performance. The experimental results of five popular CNNs on two benchmark datasets demonstrate that MOGAQNN can greatly reduce the model size and improve the classification accuracy at the same time. |
