activity
20172024
most citedLooks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Models

2 citations · 5 across the 6 of their papers we have counts for

collaborators
Showing 2021Show all

1 paper · 1 filter

cs.CV2021

Sparsity Aware Normalization for GANs

Idan Kligvasser, Tomer Michaeli

Generative adversarial networks (GANs) are known to benefit from regularization or normalization of their critic (discriminator) network during training. In this paper, we analyze…