Coupled Generative Adversarial Networks

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)

PDF Abstract NeurIPS 2016 PDF NeurIPS 2016 Abstract

Datasets


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

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet