Abstract
Unsupervised domain adaptation aims to train a good model for a target domain via transferring knowledge from a related labeled source domain, thus reducing the dependency on huge labeling of target domain samples. Generative adversarial net (GAN) is a newly proposed technique which has shown its capability of alleviating distribution discrepancy. Inspired by GAN, in this work, we propose a novel duplex GAN (DupGAN) which extracts domain invariant and discriminative representation guided by bidirectional domain transformation, formulated as a GAN with duplex discriminators. In addition, each of the duplex discriminators not only judges reality/falsity, but also performs category classification for real images to preserve the category information during domain transformation. As evaluated on the standard benchmarks, i.e., digits datasets and Office-31, our proposed DupGAN outperforms the state-of-the-art methods, indicating its effectiveness on unsupervised domain adaptation.
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Hu, L., Kan, M., Shan, S., Chen, X. (2020). Unsupervised Domain Adaptation with Duplex Generative Adversarial Network. In: Venkateswara, H., Panchanathan, S. (eds) Domain Adaptation in Computer Vision with Deep Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-45529-3_6
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