On the effect of Batch Normalization and Weight Normalization in Generative Adversarial Networks

By Sitao Xiang, Hao Li. 2017

12 citations recorded by the source corpus.

Subfields: Generative Models, Optimization & Training

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Generative Models

1. Masanori Koyama
2. Takeru Miyato
3. Tianfu Wu
4. Toshiki Kataoka
5. Yuichi Yoshida
6. Ron Banner
7. Daniel Soudry
8. Elad Hoffer

In Optimization & Training

1. Elad Hoffer
2. Masanori Koyama
3. Toshiki Kataoka
4. Itay Golan
5. Daniel Soudry
6. Yuichi Yoshida
7. Takeru Miyato
8. Ron Banner

Citations in the corpus

• Spectral Normalization for Generative Adversarial Networks (2018, background): “However, recent works in the field of GANs (Salimans et al., 2016; Xiang & Li, 2017) found their another raison d’etat as a regularizer of discriminators, and succeeded in improving the performance of the original.”
• Norm matters: efficient and accurate normalization schemes in deep networks (2018, background): “Instead, it was replaced with virtual-BN [27], weight-norm [38] and spectral normalization [31].”