Domain Adaptation Using Pseudo Labels

• URL: http://arxiv.org/abs/2402.06809v3
• Date: Tue, 12 Mar 2024 00:59:34 GMT
• Title: Domain Adaptation Using Pseudo Labels
• Authors: Sachin Chhabra, Hemanth Venkateswara and Baoxin Li
• Abstract summary: In the absence of labeled target data, unsupervised domain adaptation approaches seek to align the marginal distributions of the source and target domains. We deploy a pretrained network to determine accurate labels for the target domain using a multi-stage pseudo-label refinement procedure. Our results on multiple datasets demonstrate the effectiveness of our simple procedure in comparison with complex state-of-the-art techniques.
• Score: 16.79672078512152
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the absence of labeled target data, unsupervised domain adaptation approaches seek to align the marginal distributions of the source and target domains in order to train a classifier for the target. Unsupervised domain alignment procedures are category-agnostic and end up misaligning the categories. We address this problem by deploying a pretrained network to determine accurate labels for the target domain using a multi-stage pseudo-label refinement procedure. The filters are based on the confidence, distance (conformity), and consistency of the pseudo labels. Our results on multiple datasets demonstrate the effectiveness of our simple procedure in comparison with complex state-of-the-art techniques.

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