Generative Active Learning for Image Synthesis Personalization
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866913316415209472 |
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| author | Zhang, Xulu Zhang, Wengyu Wei, Xiao-Yong Wu, Jinlin Zhang, Zhaoxiang Lei, Zhen Li, Qing |
| author_facet | Zhang, Xulu Zhang, Wengyu Wei, Xiao-Yong Wu, Jinlin Zhang, Zhaoxiang Lei, Zhen Li, Qing |
| contents | This paper presents a pilot study that explores the application of active learning, traditionally studied in the context of discriminative models, to generative models. We specifically focus on image synthesis personalization tasks. The primary challenge in conducting active learning on generative models lies in the open-ended nature of querying, which differs from the closed form of querying in discriminative models that typically target a single concept. We introduce the concept of anchor directions to transform the querying process into a semi-open problem. We propose a direction-based uncertainty sampling strategy to enable generative active learning and tackle the exploitation-exploration dilemma. Extensive experiments are conducted to validate the effectiveness of our approach, demonstrating that an open-source model can achieve superior performance compared to closed-source models developed by large companies, such as Google's StyleDrop. The source code is available at https://github.com/zhangxulu1996/GAL4Personalization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14987 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative Active Learning for Image Synthesis Personalization Zhang, Xulu Zhang, Wengyu Wei, Xiao-Yong Wu, Jinlin Zhang, Zhaoxiang Lei, Zhen Li, Qing Computer Vision and Pattern Recognition This paper presents a pilot study that explores the application of active learning, traditionally studied in the context of discriminative models, to generative models. We specifically focus on image synthesis personalization tasks. The primary challenge in conducting active learning on generative models lies in the open-ended nature of querying, which differs from the closed form of querying in discriminative models that typically target a single concept. We introduce the concept of anchor directions to transform the querying process into a semi-open problem. We propose a direction-based uncertainty sampling strategy to enable generative active learning and tackle the exploitation-exploration dilemma. Extensive experiments are conducted to validate the effectiveness of our approach, demonstrating that an open-source model can achieve superior performance compared to closed-source models developed by large companies, such as Google's StyleDrop. The source code is available at https://github.com/zhangxulu1996/GAL4Personalization. |
| title | Generative Active Learning for Image Synthesis Personalization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.14987 |