Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Ada
目前方法的缺陷：只顧及了源域和目標域之間的邊緣分佈，沒有顧及源域和目標域之間的聯合概率分佈，不僅對齊不好，而且會把已經對齊的特徵進行負遷移。爲了解決全局對抗學習的侷限性，提出了類別級的對抗網絡。 本文貢獻如下： （1）通過提出對不同特徵的對抗損失進行自適應加權，強調了類別級特徵對齊對減少域移位的重要性。 （2）論文結果與最先進的UDA（unsupervised domain adaptation）
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