Abstract
We address the problem of multi-source unsupervised domain adaptation (MS-UDA) for the purpose of visual recognition. As opposed to single source UDA, MS-UDA deals with multiple labeled source domains and a single unlabeled target domain. Notice that the conventional MS-UDA training is based on formalizing independent mappings between the target and the individual source domains without explicitly assessing the need for aligning the source domains among themselves. We argue that such a paradigm invariably overlooks the inherent category-level correlation among the source domains which, on the contrary, is deemed to bring meaningful complementarity in the learned shared feature space. In this regard, we propose a novel approach which simultaneously (i) aligns the source domains at the class-level in a shared feature space, and (ii) maps the target domain data in the same space through an adversarially trained ensemble of source domain classifiers. Experimental results obtained on the Office-31, ImageCLEF-DA, and Office-CalTech dataset validate that our approach achieves a superior accuracy compared to state-of-the-art methods .
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Rakshit, S., Banerjee, B., Roig, G., Chaudhuri, S. (2019). Unsupervised Multi-source Domain Adaptation Driven by Deep Adversarial Ensemble Learning. In: Fink, G., Frintrop, S., Jiang, X. (eds) Pattern Recognition. DAGM GCPR 2019. Lecture Notes in Computer Science(), vol 11824. Springer, Cham. https://doi.org/10.1007/978-3-030-33676-9_34
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