Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Directory. Open Recommendations. Open API. Open Source.
Spectral Normalization for Generative Adversarial Networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida
Feb 15, 2018 (modified: Feb 16, 2018)ICLR 2018 Conference Blind Submissionreaders: everyoneShow Bibtex
Abstract:One of the challenges in the study of generative adversarial networks is the instability of its training.
In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator.
Our new normalization technique is computationally light and easy to incorporate into existing implementations.
We tested the efficacy of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques.
TL;DR:We propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator of GANs.
Keywords:Generative Adversarial Networks, Deep Generative Models, Unsupervised Learning
Enter your feedback below and we'll get back to you as soon as possible.