DCAN: Dual Channel-wise Alignment Networks for Unsupervised Scene Adaptation

Harvesting dense pixel-level annotations to train deep neural networks for semantic segmentation is extremely expensive and unwieldy at scale. While learning from synthetic data where labels are readily available sounds promising, performance degrades significantly when testing on novel realistic data due to domain discrepancies... (read more)

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