Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignment
Training a generative adversarial network (GAN) with limited data has been a challenging task. A feasible solution is to start with a GAN well-trained on a large scale source domain and adapt it to the target domain with a few samples, termed as few shot generative model adaption. However, existing...
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| Vydáno v: | Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 11194 - 11203 |
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01.06.2022
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| ISSN: | 1063-6919 |
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| Abstract | Training a generative adversarial network (GAN) with limited data has been a challenging task. A feasible solution is to start with a GAN well-trained on a large scale source domain and adapt it to the target domain with a few samples, termed as few shot generative model adaption. However, existing methods are prone to model overfitting and collapse in extremely few shot setting (less than 10). To solve this problem, we propose a relaxed spatial structural alignment (RSSA) method to calibrate the target generative models during the adaption. We design a cross-domain spatial structural consistency loss comprising the self-correlation and disturbance correlation consistency loss. It helps align the spatial structural information between the synthesis image pairs of the source and target domains. To relax the cross-domain alignment, we compress the original latent space of generative models to a subspace. Image pairs generated from the subspace are pulled closer. Qualitative and quantitative experiments show that our method consistently surpasses the state-of-the-art methods in few shot setting. Our source code: https://github.com/StevenShaw1999/RSSA. |
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| AbstractList | Training a generative adversarial network (GAN) with limited data has been a challenging task. A feasible solution is to start with a GAN well-trained on a large scale source domain and adapt it to the target domain with a few samples, termed as few shot generative model adaption. However, existing methods are prone to model overfitting and collapse in extremely few shot setting (less than 10). To solve this problem, we propose a relaxed spatial structural alignment (RSSA) method to calibrate the target generative models during the adaption. We design a cross-domain spatial structural consistency loss comprising the self-correlation and disturbance correlation consistency loss. It helps align the spatial structural information between the synthesis image pairs of the source and target domains. To relax the cross-domain alignment, we compress the original latent space of generative models to a subspace. Image pairs generated from the subspace are pulled closer. Qualitative and quantitative experiments show that our method consistently surpasses the state-of-the-art methods in few shot setting. Our source code: https://github.com/StevenShaw1999/RSSA. |
| Author | Huang, Qingming Zha, Zheng-Jun Xiao, Jiayu Li, Liang Wang, Chaofei |
| Author_xml | – sequence: 1 givenname: Jiayu surname: Xiao fullname: Xiao, Jiayu email: jiayu.xiao@vipl.ict.ac.cn organization: Inst. of Comput. Tech., CAS,Key Lab of Intell. Info. Process.,Beijing,China – sequence: 2 givenname: Liang surname: Li fullname: Li, Liang email: liang.li@ict.ac.cn organization: Inst. of Comput. Tech., CAS,Key Lab of Intell. Info. Process.,Beijing,China – sequence: 3 givenname: Chaofei surname: Wang fullname: Wang, Chaofei email: wangcf18@mails.tsinghua.edu.cn organization: Tsinghua Univesity,Department of Automation – sequence: 4 givenname: Zheng-Jun surname: Zha fullname: Zha, Zheng-Jun email: zhazj@ustc.edu.cn organization: University of Science and Technology of China,China – sequence: 5 givenname: Qingming surname: Huang fullname: Huang, Qingming email: qmhuang@ucas.ac.cn organization: Inst. of Comput. Tech., CAS,Key Lab of Intell. Info. Process.,Beijing,China |
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| Snippet | Training a generative adversarial network (GAN) with limited data has been a challenging task. A feasible solution is to start with a GAN well-trained on a... |
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| SubjectTerms | Adaptation models Codes Computational modeling Computer vision Correlation Image and video synthesis and generation; Transfer/low-shot/long-tail learning Image coding Training |
| Title | Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignment |
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