Understanding the Effectiveness of Lipschitz Constraint in Training of GANs via Gradient Analysis

By Yuxuan Song, Lantao Yu, Yong Yu. 2018

7 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. Sylvain Gelly
2. Mario Lucic
3. Ting-Li Chen
4. Xiaohua Zhai
5. Neil Houlsby
6. Marvin Ritter
7. Jinwoo Shin
8. Anna Khoreva

In Optimization & Training

1. Ting-Li Chen
2. Sylvain Gelly
3. Xiaohua Zhai
4. Neil Houlsby
5. Mario Lucic
6. Marvin Ritter

Citations in the corpus

• Self-Supervised GANs via Auxiliary Rotation Loss (2018, background): “We consider two classes of hyperparameters: First, those controlling the Lipschitz constant of the discriminator, a central quantity analyzed in the GAN literature [6, 33].”
• On Self Modulation for Generative Adversarial Networks (2018, background): “The discriminator’s Lipschitz constant is a central quantity analyzed in the GAN literature (Miyato et al., 2018; Zhou et al., 2018).”