ScoreMix: A Scalable Augmentation Strategy for Training GANs With Limited Data
文献类型：期刊论文
| 作者 | Cao, Jie1,3,4 ; Luo, Mandi1,3,4; Yu, Junchi1,3,4; Yang, Ming-Hsuan2; He, Ran1,3,4
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| 刊名 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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| 出版日期 | 2023-07-01 |
| 卷号 | 45期号:7页码:8920-8935 |
| 关键词 | Generative adversarial networks image synthesis data augmentation few-shot image-to-image translation |
| ISSN号 | 0162-8828 |
| DOI | 10.1109/TPAMI.2022.3231649 |
| 通讯作者 | He, Ran(ran.he@ia.ac.cn) |
| 英文摘要 | Generative Adversarial Networks (GANs) typically suffer from overfitting when limited training data is available. To facilitate GAN training, current methods propose to use data-specific augmentation techniques. Despite the effectiveness, it is difficult for these methods to scale to practical applications. In this article, we present ScoreMix, a novel and scalable data augmentation approach for various image synthesis tasks. We first produce augmented samples using the convex combinations of the real samples. Then, we optimize the augmented samples by minimizing the norms of the data scores, i.e., the gradients of the log-density functions. This procedure enforces the augmented samples close to the data manifold. To estimate the scores, we train a deep estimation network with multi-scale score matching. For different image synthesis tasks, we train the score estimation network using different data. We do not require the tuning of the hyperparameters or modifications to the network architecture. The ScoreMix method effectively increases the diversity of data and reduces the overfitting problem. Moreover, it can be easily incorporated into existing GAN models with minor modifications. Experimental results on numerous tasks demonstrate that GAN models equipped with the ScoreMix method achieve significant improvements. |
| 资助项目 | National Natural Science Foundation of China[6220073425] ; National Natural Science Foundation of China[U21B2045] ; National Natural Science Foundation of China[U20A20223] ; NSF CAREER[1149783] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001004665900065 |
| 出版者 | IEEE COMPUTER SOC |
| 资助机构 | National Natural Science Foundation of China ; NSF CAREER |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53630]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | He, Ran |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100045, Peoples R China 2.Univ Calif Merced, Merced, CA 95343 USA 3.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 4.Chinese Acad Sci, Inst Automat, CRIPAC, Beijing 100045, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cao, Jie,Luo, Mandi,Yu, Junchi,et al. ScoreMix: A Scalable Augmentation Strategy for Training GANs With Limited Data[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2023,45(7):8920-8935. |
| APA | Cao, Jie,Luo, Mandi,Yu, Junchi,Yang, Ming-Hsuan,&He, Ran.(2023).ScoreMix: A Scalable Augmentation Strategy for Training GANs With Limited Data.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,45(7),8920-8935. |
| MLA | Cao, Jie,et al."ScoreMix: A Scalable Augmentation Strategy for Training GANs With Limited Data".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 45.7(2023):8920-8935. |
入库方式： OAI收割
来源：自动化研究所
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