With the prevalence of pre-trained vision-language models like CLIP, leveraging the generic knowledge embedded in CLIP for domain adaptation has proved to be a promising direction. However, most existing CLIP-based methods are limited to closed-set settings. This is primarily because CLIP needs the semantic labels of unknown classes for inference, thus making it not applicable to Open-Set Domain Adaptation (OSDA). To utilize the complementary roles of CLIP and the source model, our paper proposes a novel Semantic-guided Target Adaptation (SemTA) framework for OSDA in a training-free manner. Specifically, we introduce an unknown semantic discovery module. It uses the cluster centroids of the target data to obtain the semantic labels of unknown classes from the worldwide corpus. Then, the semantic-based inference can be performed with CLIP. Additionally, the dual sample attention mechanism is implemented to output sample-based inference. Representative features from both the source model and CLIP serve as the key to improve task specificity. Compared to previous OSDA methods which reject unknown data by confidence threshold, the proposed approach is more practical and offers better interpretability. Comprehensive evaluations on four benchmarks reveal our method sets a new state-of-the-art even without training. Our code will be publicly available soon.

Cross-domain bearing fault diagnosis algorithms have garnered considerable attention in recent years due to their robust ability to address domain bias. However, prevailing methods often grapple with two key challenges: the absence of privacy preservation (necessitating access to source domain data) and the inability to facilitate real-time predictions (requiring iterative training on complete target domain data). In response to these issues, this article introduces an algorithm designed to adapt a pretrained model to the target domain in an online fashion. Notably, data augmentation is employed for pretraining the source domain model, enhancing the generalization capabilities. Subsequently, self-supervised learning is integrated through weight average updating. Furthermore, a memory bank-based approach is introduced to augment the compactness of features within the same class. Evaluation on several public datasets demonstrates that our model not only effectively enhances the diagnostic accuracy of the source model, but also achieves state-of-the-art results compared to other test-time adaptation methods.