AutoInfo GAN: Toward a better image synthesis GAN framework for high-fidelity few-shot datasets via NAS and contrastive learning

Author:

Shi JiachenORCID,Liu Wenzhen,Zhou GuoqiangORCID,Zhou Yuming

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference54 articles.

1. I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A.C. Courville, Y. Bengio, Generative adversarial nets, in: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence, K.Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada, 2014, pp. 2672–2680.

2. FIGR: few-shot image generation with reptile;Clouâtre,2019

3. Matchinggan: Matching-based few-shot image generation;Hong,2020

4. F2GAN: fusing-and-filling GAN for few-shot image generation;Hong,2020

5. Lofgan: Fusing local representations for few-shot image generation;Gu,2021

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