On the effects of batch and weight normalization in generative adversarial networks (bibtex)
by Sitao Xiang, Hao Li
Abstract:
Generative adversarial networks (GANs) are highly effective unsupervised learning frameworks that can generate very sharp data, even for data such as images with complex, highly multimodal distributions.
Reference:
On the effects of batch and weight normalization in generative adversarial networks (Sitao Xiang, Hao Li), In arXiv preprint arXiv:1704.03971, 2017.
Bibtex Entry:
@article{xiang2017effects,
	title = {On the effects of batch and weight normalization in generative adversarial networks},
	author = {Xiang, Sitao and Li, Hao},
	journal = {arXiv preprint arXiv:1704.03971},
	year = {2017},
	abstract = {Generative adversarial networks (GANs) are highly effective unsupervised learning frameworks that can generate very sharp data, even for data such as images with complex, highly multimodal distributions.}
	
}
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