Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Ada
目前方法的缺陷：只顾及了源域和目标域之间的边缘分布，没有顾及源域和目标域之间的联合概率分布，不仅对齐不好，而且会把已经对齐的特征进行负迁移。为了解决全局对抗学习的局限性，提出了类别级的对抗网络。 本文贡献如下： （1）通过提出对不同特征的对抗损失进行自适应加权，强调了类别级特征对齐对减少域移位的重要性。 （2）论文结果与最先进的UDA（unsupervised domain adaptation）
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