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We consider the problem of unsupervised domain adaptation in semantic segmentation.
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Deep adversarial attention alignment for unsupervised domain adaptation: the benefit of target expectation maximization
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Progressive growing of gans for improved quality, stability, and variation
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Semantic-aware grad-gan for virtual-to-real urban scene adaption
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Macro-micro adversarial network for human parsing
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Learning to adapt structured output space for semantic segmentation
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Unsupervised domain adaptation for semantic segmentation via class-balanced self-training
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Filter pruning via geometric median for deep convolutional neural networks acceleration
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