Faster Projected GAN: Towards Faster Few-Shot Image Generation
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911797444870144 |
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| author | Wang, Chuang Li, Zhengping Hao, Yuwen Wang, Lijun Li, Xiaoxue |
| author_facet | Wang, Chuang Li, Zhengping Hao, Yuwen Wang, Lijun Li, Xiaoxue |
| contents | In order to solve the problems of long training time, large consumption of computing resources and huge parameter amount of GAN network in image generation, this paper proposes an improved GAN network model, which is named Faster Projected GAN, based on Projected GAN. The proposed network is mainly focuses on the improvement of generator of Projected GAN. By introducing depth separable convolution (DSC), the number of parameters of the Projected GAN is reduced, the training speed is accelerated, and memory is saved. Experimental results show that on ffhq-1k, art-painting, Landscape and other few-shot image datasets, a 20% speed increase and a 15% memory saving are achieved. At the same time, FID loss is less or no loss, and the amount of model parameters is better controlled. At the same time, significant training speed improvement has been achieved in the small sample image generation task of special scenes such as earthquake scenes with few public datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08778 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Faster Projected GAN: Towards Faster Few-Shot Image Generation Wang, Chuang Li, Zhengping Hao, Yuwen Wang, Lijun Li, Xiaoxue Computer Vision and Pattern Recognition Graphics Image and Video Processing In order to solve the problems of long training time, large consumption of computing resources and huge parameter amount of GAN network in image generation, this paper proposes an improved GAN network model, which is named Faster Projected GAN, based on Projected GAN. The proposed network is mainly focuses on the improvement of generator of Projected GAN. By introducing depth separable convolution (DSC), the number of parameters of the Projected GAN is reduced, the training speed is accelerated, and memory is saved. Experimental results show that on ffhq-1k, art-painting, Landscape and other few-shot image datasets, a 20% speed increase and a 15% memory saving are achieved. At the same time, FID loss is less or no loss, and the amount of model parameters is better controlled. At the same time, significant training speed improvement has been achieved in the small sample image generation task of special scenes such as earthquake scenes with few public datasets. |
| title | Faster Projected GAN: Towards Faster Few-Shot Image Generation |
| topic | Computer Vision and Pattern Recognition Graphics Image and Video Processing |
| url | https://arxiv.org/abs/2403.08778 |