A curated list of resources including papers, datasets, and relevant links pertaining to few-shot image generation.
Contributions are welcome. If you wish to contribute, feel free to send a pull request. If you have suggestions for new sections to be included, please raise an issue and discuss before sending a pull request.
- Louis Clouâtre, Marc Demers: "FIGR: Few-shot Image Generation with Reptile." CoRR abs/1901.02199 (2019) [pdf] [code]
- Weixin Liang, Zixuan Liu, Can Liu: "DAWSON: A Domain Adaptive Few Shot Generation Framework." CoRR abs/2001.00576 (2020) [pdf] [code]
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Yan Hong, Li Niu, Jianfu Zhang, Weijie Zhao, Chen Fu, Liqing Zhang: "F2GAN: Fusing-and-Filling GAN for Few-shot Image Generation." ACM MM (2020) [pdf]
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Yan Hong, Li Niu, Jianfu Zhang, Liqing Zhang: "MatchingGAN: Matching-based Few-shot Image Generation." ICME (2020) [pdf]
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Sergey Bartunov, Dmitry P. Vetrov: "Few-shot Generative Modelling with Generative Matching Networks." AISTATS (2018) [pdf] [code]
- Antreas Antoniou, Amos J. Storkey, Harrison Edwards: "Data Augmentation Generative Adversarial Networks." CoRR abs/1711.04340 (2017) [pdf] [code]
- Omniglot: 1623 handwritten characters from 50 different alphabets. Each of the 1623 characters was drawn online via Amazon's Mechanical Turk by 20 different people [link]
- EMNIST: 47 balanced classes [link]
- FIGR: 17,375 classes of 1,548,256 images representing pictograms, ideograms, icons, emoticons or object or conception depictions [link]
- VGG-Faces: 2395 categories [link]
- Flowers: 8189 images from 102 flower classes [link]
- Animal Faces: 117574 images from 149 animal classes [link]