Dual-discriminator adversarial framework for data-free quantization
文献类型：期刊论文
| 作者 | Li, Zhikai1,2 ; Ma, Liping2; Long, Xianlei1,2; Xiao, Junrui1,2; Gu, Qingyi2
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2022-10-28 |
| 卷号 | 511页码:67-77 |
| ISSN号 | 0925-2312 |
| 关键词 | Model compression Quantized neural networks Data-free quantization |
| DOI | 10.1016/j.neucom.2022.09.076 |
| 通讯作者 | Gu, Qingyi(qingyi.gu@ia.ac.cn) |
| 英文摘要 | Thanks to the potential to address the privacy and security issues, data-free quantization that generates samples based on the prior information in the model has recently been widely investigated. However, existing methods failed to adequately utilize the prior information and thus cannot fully restore the real-data characteristics and provide effective supervision to the quantized model, resulting in poor performance. In this paper, we propose Dual-Discriminator Adversarial Quantization (DDAQ), a novel data-free quantization framework with an adversarial learning style that enables effective sample generation and learning of the quantized model. Specifically, we employ a generator to produce meaningful and diverse samples directed by two discriminators, aiming to facilitate the matching of the batch normalization (BN) distribution and maximizing the discrepancy between the full-precision model and the quantized model, respectively. Moreover, inspired by mixed-precision quantization, i.e., the importance of each layer is different, we introduce layer importance prior to both discriminators, allowing us to make better use of the information in the model. Subsequently, the quantized model is trained with the generated samples under the supervision of the full-precision model. We evaluate DDAQ on various network structures for different vision tasks, including image classification and object detection, and the experimental results show that DDAQ outperforms all baseline methods with good generality. (C) 2022 Elsevier B.V. All rights reserved. |
| 资助项目 | Scientific Instrument Developing Project of the Chinese Academy of Sciences[YJKYYQ20200045] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000871948700006 |
| 资助机构 | Scientific Instrument Developing Project of the Chinese Academy of Sciences |
| 源URL | [http://ir.ia.ac.cn/handle/173211/50523]  |
| 专题 | 精密感知与控制研究中心_精密感知与控制 |
| 通讯作者 | Gu, Qingyi |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Jingjia Rd, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, East Zhongguancun Rd, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Zhikai,Ma, Liping,Long, Xianlei,et al. Dual-discriminator adversarial framework for data-free quantization[J]. NEUROCOMPUTING,2022,511:67-77. |
| APA | Li, Zhikai,Ma, Liping,Long, Xianlei,Xiao, Junrui,&Gu, Qingyi.(2022).Dual-discriminator adversarial framework for data-free quantization.NEUROCOMPUTING,511,67-77. |
| MLA | Li, Zhikai,et al."Dual-discriminator adversarial framework for data-free quantization".NEUROCOMPUTING 511(2022):67-77. |
入库方式： OAI收割
来源：自动化研究所
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