Incremental Unsupervised Domain Adaptation Through Optimal Transport

In this paper, we address the problem of unsupervised domain adaptation where we ask to infer a low target risk classifier, while labeled data are only available from the source domain. Our proposed approach, called DA-OTP, aims to learn a gradual subspace alignment of the source and target domains through Supervised Locality Preserving Projection, so that projected data in the joint low-dimensional latent subspace can be domain-invariant and easily separable. However, this objective can be rather challenging to achieve because of the absence of labeled data in the target domain. To overcome this conundrum, we use an incremental label propagation technique based on optimal transport, which performs selective pseudo-labeling in the target domain. The selected pseudo-labeled target samples are then combined with labeled source samples to learn in a self-training fashion a robust classifier after the incremental subspace alignment. Experiments show the competitiveness of the proposed approach across contemporary state-of-the-art methods over a range of domain adaptation problems. We make our code publicly available.
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Code is available at: https://github.com/DA-OTP/DA-OTP