Unsupervised Domain Adaptation without Source Data by Casting a BAIT
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Unsupervised domain adaptation (UDA) aims to transfer the knowledge learned from labeled source domain to unlabeled target domain. Existing UDA methods require access to the data from the source domain, during adaptation to the target domain, which may not be feasible in some real-world situations. In this paper, we address Source-free Unsupervised Domain Adaptation (SFUDA), where the model has no access to any source data during the adaptation period. We propose a novel framework named BAIT to tackle SFUDA. Specifically, we first train the model on source domain. With the source-specific classifier head (referred to as anchor classifier) fixed, we further introduce a new learnable classifier head (referred to as bait classifier), which is initialized by the anchor classifier. When adapting the source model to the target domain, the source data are no more accessible and the bait classifier aims to push the target features towards the right side of the decision boundary of the anchor classifier, thus achieving the feature alignment. Experiment results show that proposed BAIT achieves state-of-the-art performance compared with existing normal UDA methods and several SFUDA methods.
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