Local Uncertainty Energy Transfer for Active Domain Adaptation.

Active Domain Adaptation (ADA) improves knowledge transfer efficiency from the labeled source domain to the unlabeled target domain by selecting a few target sample labels. However, most existing active sampling methods ignore the local uncertainty of neighbors in the target domain,making it easier to pick out anomalous samples that are detrimental to the model. To address this problem, we present a new approach to active domain adaptation called Local Uncertainty Energy Transfer (LUET), which integrates active learning of local uncertainty confusion and energy transfer alignment constraints into a unified framework. First, in the active learning module, the uncertainty difficult and representative samples from the target domain are selected through local uncertainty energy selection and entropy-weighted class confusion selection. And the active learning strategy based on local uncertainty energy will avoid selecting anomalous samples in the target domain. Second, for the discrimination issue caused by domain shift, we use a global and local energy-transfer alignment constraint module to eliminate the domain gap and improve accuracy. Finally, we used negative log-likelihood loss for supervised learning of source domains and query samples. With the introduction of sample-based energy metrics, the active learning strategy is more closely with the domain alignment. Experiments on multiple domain-adaptive datasets have demonstrated that our LUET can achieve outstanding results and outperform existing state-of-the-art approaches.