Exploring conditional pixel-independent generation in GAN inversion for image processing

Author:

Huang Chunyao,Sun Xiaomei,Tian Zhiqiang,Du Shaoyi,Zeng WeiORCID

Funder

Natural Science Foundation of Fujian Province

External Collaboration Project of Science and Technology Department of Fujian Province

Fujian Province Chinese Academy of Sciences STS Program Supporting Project

Guidance Project of the Science and Technology Department of Fujian Province

Qimai Science and Technology Innovation Project of Wuping Country

Xinluo District Industry-University-Research Science and Technology Joint Innovation Project

Special Project of the Ministry of Education’s Higher Education Science Research and Development Center

Publisher

Springer Science and Business Media LLC

Reference34 articles.

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2. Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434

3. Karras T, Aila T, Laine S, Lehtinen J (2018) Progressive growing of GANs for improved quality, stability, and variation. In: International conference on learning representations. pp 1880–1900

4. Shi J, Liu W, Zhou G, Zhou Y (2023) AutoInfo GAN: toward a better image synthesis GAN framework for high-fidelity few-shot datasets via NAS and contrastive learning. Knowl-Based Syst 276:110757

5. Karras T, Laine S, Aila T (2019) A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp 4401–4410

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