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...
Main Authors: | Liu, Steven, Wang, Tongzhou, Bau, David, Zhu, Jun-Yan, Torralba, Antonio |
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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
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Online Access: | https://hdl.handle.net/1721.1/137599 |
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