Diverse Image Generation via Self-Conditioned GANs

© 2020 IEEE. We introduce a simple but effective unsupervised method for generating diverse images. We train a class-conditional GAN model without using manually annotated class labels. Instead, our model is conditional on labels automatically derived from clustering in the discriminator's feat...

Full description

Bibliographic Details
Main Authors: Liu, Steven, Wang, Tongzhou, Bau, David, Zhu, Jun-Yan, Torralba, Antonio
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2021
Online Access:https://hdl.handle.net/1721.1/137599