Coupled Generative Adversarial Networks

NeurIPS 2016 Ming-Yu LiuOncel Tuzel

We propose coupled generative adversarial network (CoGAN) for learning a joint distribution of multi-domain images. In contrast to the existing approaches, which require tuples of corresponding images in different domains in the training set, CoGAN can learn a joint distribution without any tuple of corresponding images... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Image-to-Image Translation Cityscapes Labels-to-Photo CoGAN Class IOU 0.06 # 3
Per-class Accuracy 10% # 3
Per-pixel Accuracy 40% # 8
Image-to-Image Translation Cityscapes Photo-to-Labels CoGAN Per-pixel Accuracy 45% # 4
Per-class Accuracy 11% # 4
Class IOU 0.08 # 3