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Training on synthetic data is becoming popular in vision due to the convenient acquisition of accurate pixel-level labels. But the domain gap between synthetic and real images significantly degrades the performance of the trained model. We propose a color space adaptation method to bridge the gap. A set of closed-form operations are adopted to make color space adjustments while preserving the labels. We embed these operations into a two-stage learning approach, and demonstrate the adaptation efficacy on the semantic segmentation task of cirrus clouds.

Learning color space adaptation from synthetic to real images of cirrus clouds