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.

Unsupervised domain adaptation (UDA) has emerged as a promising technique for transferring knowledge from a labeled domain to an unlabeled domain. However, existing UDA methods are severely constrained by data privacy and semantic inconsistencies. To alleviate these limitations, this work challenges the Source-Free Open-Set Domain Adaptation (SF-OSDA), where the pre-trained source model is directly leveraged on the open target domain for adaptation. For this purpose, we introduce the novel Dynamic Target Distribution Estimation (DTDE) method, which effectively performs known classification and unknown separation through self-supervised learning with prototypes. To construct known prototypes, a self-adaptive sampling strategy is employed to consider the category disparity. For unknown prototypes, we utilize a self-splitting and excluding principle to bypass the unknown semantics problem. Specifically, self-splitting is to evaluate the overall clustering distribution of the target domain. By excluding clusters resembling known prototypes, the remaining cluster centroids can serve as unknown prototypes. The superiority of our approach is validated across multiple benchmarks. Remarkably, DTDE outperforms the best competitor by 7.6% on the VisDA dataset.

Over the past decade, domain adaptation has become a widely studied branch of transfer learning which aims to improve performance on target domains by leveraging knowledge from the source domain. Conventional domain adaptation methods often assume access to both source and target domain data simultaneously, which may not be feasible in real-world scenarios due to privacy and confidentiality concerns. As a result, the research of Source-Free Domain Adaptation (SFDA) has drawn growing attention in recent years, which only utilizes the source-trained model and unlabeled target data to adapt to the target domain. Despite the rapid explosion of SFDA work, there has been no timely and comprehensive survey in the field. To fill this gap, we provide a comprehensive survey of recent advances in SFDA and organize them into a unified categorization scheme based on the framework of transfer learning. Instead of presenting each approach independently, we modularize several components of each method to more clearly illustrate their relationships and mechanisms in light of the composite properties of each method. Furthermore, we compare the results of more than 30 representative SFDA methods on three popular classification benchmarks, namely Office-31, Office-home, and VisDA, to explore the effectiveness of various technical routes and the combination effects among them. Additionally, we briefly introduce the applications of SFDA and related fields. Drawing on our analysis of the challenges confronting SFDA, we offer some insights into future research directions and potential settings.

Continual test-time domain adaptation (TTA) is a challenging topic in the field of source-free domain adaptation, which focuses on addressing cross-domain multimedia information during inference with a continuously changing data distribution. Previous methods have been found to lack noise robustness, leading to a significant increase in errors under strong noise. In this paper, we address the noise-robustness problem in continual TTA by offering three effective recipes to mitigate it. At the category level, we employ the Taylor cross-entropy loss to alleviate the low confidence category bias commonly associated with cross-entropy. At the sample level, we reweight the target samples based on uncertainty to prevent the model from overfitting on noisy samples. Finally, to reduce pseudo-label noise, we propose a soft ensemble negative learning mechanism to guide the model optimization using ensemble complementary pseudo labels. Our method achieves state-of-the-art performance on three widely used continual TTA datasets, particularly in the strong noise setting that we introduced.

The bi-classifier paradigm is widely adopted as an adversarial method to address domain shift challenge in unsupervised domain adaptation (UDA) by evenly training two classifiers. In this paper, we report that although the generalization ability of the feature extractor can be strengthened by the two even classifiers, the decision boundaries of the two classifiers would be shrank to the source domain in the adversarial process, which weakens the discriminative ability of the learned model. To tame this dilemma, we disentangle the function of the two classifiers and introduce uneven bi-classifier learning for domain adaptation. Specifically, we leverage the F-norm (Frobenius Norm) of classifier predictions instead of the classifier disagreement to achieve adversarial learning. By this way, our feature extractor can be adversarially trained with a single classifier and the other classifier is used for preserving the target-specific decision boundaries. The proposed uneven bi-classifier learning protocol can simultaneously enhance the generalization ability of the feature extractor and expand the decision boundary of the target classifier. Extensive experiments on large-scale datasets prove that our method can significantly surpass previous domain adaptation methods, even with only a single classifier being involved.

The bi-classifier paradigm is a common practice in unsupervised domain adaptation (UDA), where two classifiers are leveraged to guide the model to learn domain invariant features. Previous approaches only focused on the consistency of the outputs between classifiers, but ignored the classification certainty of each classifier. Therefore, existing methods in some cases may mislead the classifiers into the wrong direction of ambiguous outputs and subsequently undermine the discriminability. To challenge this problem, in this paper we propose Classification Certainty Maximization (CCM) which considers both the joint certainty between classifiers and the individual certainty of each classifier via a novel formulation, and derived their optimal weight ratios by theoretical derivation from the perspective of gradient. In addition, we also propose a dynamic centroid update strategy to mitigate the domain gaps at the feature level. Extensive experiments on four widely used UDA datasets show that CCM performs better than the existing state-of-the-art domain adaptation methods. Notably, our dynamic centroid update strategy can be used as a plug-and-play module for existing bi-classifier domain adaptation methods to boost classification accuracy.

In this article, the fuzzy pinning adaptive event-triggered control (FPAETC) problem related to complex networked systems is investigated. In light of the fact that the underlying controllers are fuzzy pinning, and the threshold parameters can flexibly update their states based on the latest and current sampled signals rather than transmitted signals, a novel state-dependent FPAETC protocol is devised to reduce the frequency of event-triggering and save more communication resources. Moreover, a more general time-dependent AETC protocol is proposed compared to the existing related results. Then, in combination with the graph theory, looped Lyapunov functional method, and fuzzy logic pinning technique, some relaxed criteria that establish the relationship between the synchronization and desired$\mathcal {H}_{\infty }$performance for the considered system are derived. Simulation results validate the superiority of derived theoretical results.