Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences,Mathematics (miscellaneous)
Reference60 articles.
1. Arjovsky M, Bottou L (2017) Towards principled methods for training generative adversarial networks. arxiv e-prints, art. arXiv preprint arXiv:1701.04862
2. Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: International conference on machine learning, PMLR. pp 214–223
3. Bao J, Chen D, Wen F, Li H, Hua G (2017) Cvae-gan: fine-grained image generation through asymmetric training. In: Proceedings of the IEEE international conference on computer vision. pp 2745–2754
4. Bao J, Li L, Redoloza F (2020) Coupling ensemble smoother and deep learning with generative adversarial networks to deal with non-gaussianity in flow and transport data assimilation. J Hydrol 590:125443
5. Bau D, Zhu JY, Wulff J, Peebles W, Strobelt H, Zhou B, Torralba A (2019) Seeing what a gan cannot generate. In: Proceedings of the IEEE international conference on computer vision. pp 4502–4511
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