On the effects of batch and weight normalization in generative adversarial networks (bibtex)

by Xiang, Sitao and Li, Hao

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 (Xiang, Sitao and Li, Hao), 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.}
	
}