UCSD and NVIDIA AI Researchers Propose ‘CoordGAN’: a Novel Disentangled GAN Mode That Produces Dense Correspondence Maps Represented by a Novel Coordinate Space

GANs (Generative Adversarial Networks) have had a lot of success synthesizing high-quality images, and a lot of recent research shows that they also learn a lot of interpretable directions in the latent space. Moving latent codes in a semantically relevant direction (e.g., posture) produces instances with smooth fluctuating appearance (e.g., constantly changing views), signaling that GANs implicitly learn which pixels or regions correspond to each other from different synthesized examples.

Instead, a dense correlation is created between semantically equivalent local …

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