Domain Gap Estimation for Source Free Unsupervised Domain Adaptation with Many Classifiers

  • Proposed to use many classifiers as a strong domain discriminator for source free unsupervised domain adaptation. Not only the optimal number of classifiers is provided, but also the trade-off between domain gap estimation and limited source domain training data is rigorously discussed.

  • Theoretically answered why using many classifiers can measure the domain gap more accurately and stably than bi-classifier in source free setting.

  • Proposed a framework of source free unsupervised domain adaptation via many classifiers (DAMC). By exploiting many classifiers in source domain training, the pre-trained source model approaches the tightest domain gap which makes the downstream target adaptation better performance.

Figure 1: Comparison of disagreement ratio between bi-classifier model and 3-classifier model for 3-category task. 3-classifier model gives a lower disagreement ratio than bi-classifier model, which induces a tighter domain gap estimation

Ziyang Zong, Jun He, Lei Zhang, Hai Huan. Domain Gap Estimation for Source Free Unsupervised Domain Adaptation with Many Classifiers. https://arxiv.org/abs/2207.05785 (under review)

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