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杨宗翰
ADDA: Adversarial Discriminative Domain Adaptation
2019-09-20 18:54:09
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# Introduction CVPR2017. They want to do Adversarial Discriminative Domain Adaptation. # Structure As usual, there's Source Domain(the old one) $S$ and Target Domain(the new one) $T$, and a Classifier $C$ on Source Domain, Target Representations $M_t$ and $M_s$, a Discriminator $D$. The loss is straight forward.  # Training method  1. Train $M_s$ and $C$ on $S$. 2. Keep $M_s$, initialize $M_t$ wth $M_s$ and train $D$ and $M_t$. 3. Tests with $M_t$ and $C$. # Experiments  Recall that CYCADA achived 90% on SVHN → MNIST.
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CYCADA: CYCLE-CONSISTENT ADVERSARIAL DOMAIN ADAPTATION
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