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CR-GAN: Learning Complete Representations for Multi-view Generation
[article]
2018
arXiv
pre-print
Generating multi-view images from a single-view input is an essential yet challenging problem. It has broad applications in vision, graphics, and robotics. Our study indicates that the widely-used generative adversarial network (GAN) may learn "incomplete" representations due to the single-pathway framework: an encoder-decoder network followed by a discriminator network. We propose CR-GAN to address this problem. In addition to the single reconstruction path, we introduce a generation sideway
arXiv:1806.11191v1
fatcat:bro7smpffnbbvh5ldhjmfu6xcu