Abstract
Unsupervised domain adaptation aims to learn a classification model from the source domain with much-supervised information, which is applied to the utterly unsupervised target domain. However, collecting enough labeled source samples is difficult in some scenarios, decreasing the effectiveness of previous approaches substantially. Therefore, a more challenging and applicable problem called few-shot unsupervised domain adaptation is considered in this work, where a classifier trained with only a few source labels needs to show strong generalization on the target domain. The prototype-based self-supervised learning method has presented superior performance improvements in addressing this problem, while the quality of the prototype could be further improved. To mitigate this situation, a novel Prototype-Augmented Contrastive Learning is proposed. A new computation strategy is utilized to rectify the source prototypes, which are then used to improve the target prototypes. To better learn semantic information and align features, both in-domain prototype contrastive learning and cross-domain prototype contrastive learning are performed. Extensive experiments are conducted on three widely used benchmarks: Office, OfficeHome, and DomainNet, achieving accuracy improvement of over 3%, 1%, and 0.5%, respectively, demonstrating the effectiveness of the proposed method.
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Gong, L., Zhang, W., Li, M., Zhang, J., Zhang, Z. (2023). Prototype-Augmented Contrastive Learning for Few-Shot Unsupervised Domain Adaptation. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14120. Springer, Cham. https://doi.org/10.1007/978-3-031-40292-0_17
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