Learning from Simulated and Unsupervised Images through Adversarial Training

CVPR 2017 Ashish ShrivastavaTomas PfisterOncel TuzelJosh SusskindWenda WangRuss Webb

With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations. However, learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions... (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 SimGAN Class IOU 0.04 # 4
Per-class Accuracy 10% # 3
Per-pixel Accuracy 20% # 9
Image-to-Image Translation Cityscapes Photo-to-Labels SimGAN Per-pixel Accuracy 47% # 3
Per-class Accuracy 11% # 4
Class IOU 0.07 # 4