GMM-COMET：通过均值教师和基于高斯混合模型的伪标签的连续源无关通用领域适应
GMM-COMET: Continual Source-Free Universal Domain Adaptation via a Mean Teacher and Gaussian Mixture Model-Based Pseudo-Labeling
AI总结：
GMM-COMET通过均值教师和高斯混合模型伪标签方法，实现连续源无关通用领域适应，提升长序列适应的稳定性与鲁棒性。
摘要：
无监督领域适应解决训练数据和测试数据之间领域偏移对神经网络性能的影响问题。在现实场景中，源数据可能在适应过程中不再可用，且目标域的标签空间可能与源标签空间不同。这种设置被称为源无关通用领域适应（SF-UniDA），但现有方法仅假设单一领域偏移。本文首次研究了连续SF-UniDA，模型必须依次适应多个不同未标记目标域的流。基于我们之前的方法，我们结合其关键思想，通过在均值教师框架内整合基于高斯混合模型的伪标签来提高长序列适应的稳定性。此外，我们引入一致性损失以进一步增强鲁棒性。所提出的方法GMM-COMET为连续SF-UniDA提供了有力的基础线，并是唯一在所有评估场景中持续优于源模型的方法。我们的代码可在https://github.com/pascalschlachter/GMM-COMET上获取。
英文摘要：
Unsupervised domain adaptation tackles the problem that domain shifts between training and test data impair the performance of neural networks in many real-world applications. Thereby, in realistic scenarios, the source data may no longer be available during adaptation, and the label space of the target domain may differ from the source label space. This setting, known as source-free universal domain adaptation (SF-UniDA), has recently gained attention, but all existing approaches only assume a single domain shift from source to target. In this work, we present the first study on continual SF-UniDA, where the model must adapt sequentially to a stream of multiple different unlabeled target domains. Building upon our previous methods for online SF-UniDA, we combine their key ideas by integrating Gaussian mixture model-based pseudo-labeling within a mean teacher framework for improved stability over long adaptation sequences. Additionally, we introduce consistency losses for further robustness. The resulting method GMM-COMET provides a strong first baseline for continual SF-UniDA and is the only approach in our experiments to consistently improve upon the source-only model across all evaluated scenarios. Our code is available at https://github.com/pascalschlachter/GMM-COMET.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
