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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 ![title](https://leanote.com/api/file/getImage?fileId=5d91a51bab6441478f0014c7) * 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 . ![title](https://leanote.com/api/file/getImage?fileId=5d91abfdab6441498d001599) ## In supervised case ![title](https://leanote.com/api/file/getImage?fileId=5d91b2d0ab6441498d0015e6) Similarly, there's ![title](https://leanote.com/api/file/getImage?fileId=5d91b33fab6441478f001577) ## In unsupervised case ![title](https://leanote.com/api/file/getImage?fileId=5d91b639ab6441478f0015a8) # Performance $MNIST$ domain is limited to only 10 samples per class, denoted $MNIST-(10)$ ![title](https://leanote.com/api/file/getImage?fileId=5d91b721ab6441478f0015b3) ![title](https://leanote.com/api/file/getImage?fileId=5d91b78cab6441498d001627) ![title](https://leanote.com/api/file/getImage?fileId=5d91d2c7ab6441478f0016cd)
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