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杨宗翰
AUGMENTED CYCLIC ADVERSARIAL LEARNING FOR LOW RESOURCE DOMAIN ADAPTATION
2019-09-30 14:43:45
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# Background * Solving low resource domain adaptation. * Assume has few lable for Target Domain. # Structure  * the ultimate goal of our approach is to use the mapped Source → Target samples ($X_{S→T}$ ) to augment the limited data of the target domain ($X_T$ ). * let $M_S$ and $M_T$ be the task specific models trained on domains $P_S(X, Y)$ and $P_T (X, Y)$. ## Relaxed Cycle Consistency .  ## In supervised case  Similarly, there's  ## In unsupervised case  # Performance $MNIST$ domain is limited to only 10 samples per class, denoted $MNIST-(10)$   
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