Fuzzy model-based domain adaptation has gained increasing attention for its ability to handle uncertainty arising from distribution shifts during knowledge transfer. However, existing fuzzy domain adaptation methods primarily focus on transferring information within the same data modality, leaving a gap in extending fuzzy domain adaptation to cross-modal scenarios. In addition, fuzzy domain adaptation across multiple domains still requires further exploration to effectively identify and select source models that are more relevant to the target domain. To address these gaps, this paper proposes a target-oriented autonomous fuzzy model adaptation method built upon a pre-trained multimodal foundation model, leveraging both visual and textual modalities. Simultaneously, an autonomous source selection strategy is developed by measuring similarities between each pair of source and target domains using fuzzy memberships, thereby enabling a multi-layer fuzzy rule structure guided by the target domain. The proposed method is evaluated on three widely used public datasets, demonstrating the effectiveness of incorporating fuzzy rules and multimodal information.

Prompt Tuning adapts frozen models to new tasks by prepending a few learnable embeddings to the input. However, it struggles with tasks that suffer from data scarcity. To address this, we explore Cross-Modality Prompt Transfer, leveraging prompts pretrained on a data-rich modality to improve performance on data-scarce tasks in another modality. As a pioneering study, we first verify the feasibility of cross-modality prompt transfer by directly applying frozen source prompts (trained on the source modality) to the target modality task. To empirically study cross-modality prompt transferability, we train a linear layer to adapt source prompts to the target modality, thereby boosting performance and providing ground-truth transfer results. Regarding estimating prompt transferability, existing methods show ineffectiveness in cross-modality scenarios where the gap between source and target tasks is larger. We address this by decomposing the gap into the modality gap and the task gap, which we measure separately to autonomously select the best source prompt for a target task. Additionally, we propose Attention Transfer to further reduce the gaps by injecting target knowledge into the prompt and reorganizing a top-transferable source prompt using an attention block. We conduct extensive experiments involving prompt transfer from 13 source language tasks to 19 target vision tasks under three settings. Our findings demonstrate that: (i) cross-modality prompt transfer is feasible, supported by in-depth analysis; (ii) measuring both the modality and task gaps is crucial for accurate prompt transferability estimation, a factor overlooked by previous studies; (iii) cross-modality prompt transfer can significantly release the powers of prompt tuning on data-scarce tasks, as evidenced by comparisons with a newly released prompt-based benchmark.

Unsupervised domain adaptation that relies on data matching can raise privacy concerns when leveraging transferable knowledge from the source domain(s). To address this issue, source-free domain adaptation has been proposed and subsequently developed. However, privacy risks persist due to potential data leaks from model inversion attacks even when using only source models without accessing the source data directly. Alongside this, another underexplored problem is feature heterogeneity across multiple source domains. In this article, we propose a fuzzy domain adaptation method, fuzzy heterogeneous domain adaptation (FuzHDA), that learns fuzzy rules from heterogeneous teacher models, even when these models are black boxes. The proposed method first trains heterogeneous source models privately on individual devices without sharing any information, including neither data nor model parameters. Then, using a memory bank that stores all target predictions from the pretrained source models, we apply a self-knowledge distillation approach to train a target model, which simulates the predictions from these heterogeneous source teachers. After completing the target model, a cross-modality hybrid model leveraging prompt learning and uncovering causal factors is fine-tuned via self-supervision at the last step to facilitate transfer performance. Experiments on real-world datasets demonstrate the superiority of the proposed FuzHDA.

Domain adaptation with inaccurate label is a challenging and interesting topic in transfer learning, dealing with source and target domains with shift label spaces. Most existing domain adaptation methods assume aware label distributions among source and target domains. However, this cannot always be guaranteed in reality. Furthermore, existing multi-domain adaptation methods rarely deal with label heterogeneity among source domains. Thus, in this paper, we propose a multi-source domain adaptation method handling Inaccurate label (IncLabDA) during transfer. The proposed method designs a module that can transfer knowledge from multi-source domains with both homogeneous and heterogeneous label spaces in universal scenario. Anchors are generated from pre-trained model to build data-matching via a contrastive method avoiding to referring original data. In addition, class center consistency combined with clustering strategy considering both global and local confidences is adopted to recognize out-of-distribution samples. By removing source private classes and target unknown samples, highly confident target samples are collected to self-supervise the adaptation. At the same time, constraints enlarging the distance among target known classes and between the known and unknown samples are applied to enhance the performance of the proposed model. Experiments on real-world datasets validate the superiority of the IncLabDA model.

Unsupervised multidomain adaptation attracts increasing attention as it delivers richer information when tackling a target task from an unlabeled target domain by leveraging the knowledge attained from labeled source domains. However, it is the quality of training samples, not just the quantity, that influences transfer performance. In this article, we propose a multidomain adaptation method with sample and source distillation (SSD), which develops a two-step selective strategy to distill source samples and define the importance of source domains. To distill samples, the pseudo-labeled target domain is constructed to learn a series of category classifiers to identify transfer and inefficient source samples. To rank domains, the agreements of accepting a target sample as the insider of source domains are estimated by constructing a domain discriminator based on selected transfer source samples. Using the selected samples and ranked domains, transfer from source domains to the target domain is achieved by adapting multilevel distributions in a latent feature space. Furthermore, to explore more usable target information which is expected to enhance the performance across domains of source predictors, an enhancement mechanism is built by matching selected pseudo-labeled and unlabeled target samples. The degrees of acceptance learned by the domain discriminator are finally employed as source merging weights to predict the target task. Superiority of the proposed SSD is validated on real-world visual classification tasks.

Unsupervised domain adaptation leverages knowledge from source domain(s)/task(s) to facilitate learning in target task, particularly in unsatisfied and complex scenarios with data scarcity and distribution shifts. This approach helps reduce the high costs associated with collecting or labeling data for the target domain. However, it raises privacy concerns due to its matching techniques requiring access to source data, particularly in sensitive applications. In addition, most domain adaptation methods assume that source and target domains share the same label space, disregarding category shifts. In this article, we propose federated fuzzy transfer learning for category shifts (FdFTL) to address the before mentioned challenges-data privacy and category shifts. By combining a hybrid approach of fuzzy model and federated learning, a cloud model capable of performing across domains can be trained without the need for data sharing. This approach also results in a reduction of model parameters compared to traditional methods training individual models from multiple source domains. To eliminate domain and category shifts, we utilize a global clustering and a local semantic consensus clustering to effectively separate known target classes from out-of-distribution samples. Furthermore, we incorporate a confident score and the Silhouette analysis to elaborate the accuracy of categorizing target known classes. Experimental results on real-world visual tasks across universal, open-set, partial, and closed-set scenarios demonstrate the effectiveness of our proposed method.

Domain adaptation is a practicable tool in real world application where there exists data scarcity. Multi-source domain adaptation attracts increasing attention due to its ability to enrich transfer knowledge by combining information from multiple domains. However, knowledge transfer can trigger privacy concerns by accessing source data. In addition, multiple domains can have label heterogeneity problem. In this paper, to solve the mentioned problems, we conduct an incomplete multi-source domain adaptation (IMSDA) method which can address transfer learning with and without the access to source data. As far as we are aware, this is the first work handling source-free incomplete domain adaptation. To take the benefits of multiple sources, multi-task learning is adopted to learn a general source model which can perform on multiple domains. A data matching strategy with and without source data forcing target sample to source latent feature space is developed to combine with self-supervision to adapt source model to the target domain. Experiments on real-world datasets indicate the superiority of the proposed method.

Unsupervised domain adaptation deals with a task from an unlabeled target domain by leveraging the knowledge gained from labeled source domain(s). The fuzzy system is adopted in domain adaptation to better tackle the uncertainty caused by information scarcity in the transfer. Most existing fuzzy and nonfuzzy domain adaptation methods depend on data-level distribution matching to eliminate the domain shift. However, data sharing can trigger privacy concerns. This situation results in the unavailability of source data, wherein most domain adaptation methods cannot be applied. Source-free domain adaptation is then proposed to handle this problem. But the existing source-free domain adaptation methods rarely deal with any soft information component due to data imprecision. Besides, fewer methods handle multiple source domains that provide richer transfer information. Thus, in this article, we propose source-free multidomain adaptation with fuzzy rule-based deep neural networks, which takes advantage of a fuzzy system to handle data uncertainty in domain adaptation without source data. To learn source private models with high generality, which is important to collect low-noise pseudotarget labels, auxiliary tasks are designed by jointly training source models from multiple domains, which share source parameters and fuzzy rules while protecting source data. To transfer fuzzy rules and fit source private parameters to the target domain, self-supervised learning and anchor-based alignment are built to force target data into source feature spaces. Experiments on real-world datasets under both homogeneous and heterogeneous label space scenarios are carried out to validate the proposed method. The results indicate the superiority of the proposed fuzzy rule-based source-free multidomain adaptation method.

Unsupervised domain adaptation leverages the previously gained knowledge from a labeled source domain to tackle the task from a different but similar unlabeled target domain. Most existing methods focus on transferring knowledge from a single source domain, but the information from a single domain may be inadequate to complete the target task. Some previous studies have turned to multi-view representations to enrich the transferable information. However, they simply concatenate multi-view features, which may result in information redundancy. In this paper, we propose a dynamic classifier alignment (DCA) method for multi-source domain adaptation, which aligns classifiers driven from multi-view features via a sample-wise automatic way. As proposed, both the importance of each view and the contribution of each source domain are investigated. To determine the important degrees of multiple views, an importance learning function is built by generating an auxiliary classifier. To learn the source combination parameters, a domain discriminator is developed to estimate the probability of a sample belonging to multiple source domains. Meanwhile, a self-training strategy is proposed to enhance the cross-domain ability of source classifiers with the assistance of pseudo target labels. Experiments on real-world visual datasets show the superiority of the proposed DCA.

Unsupervised domain adaptation transfers gained knowledge from labeled source domain(s) to a similar unlabeled target domain by eliminating the domain shifts. Most existing domain adaptation methods require the access to source data to match the source and target distributions. However, data privacy concerns make it difficult or impossible to share source data, leading to failures in existing domain adaptation methods. Admittedly, a few previous studies deal with domain adaptation without source data, but they rarely pay heed to data free domain adaptation with multiple source domains containing richer knowledge. In this paper, we propose a new multi-source data-free domain adaptation method- generally auxiliary model training (GAM)- which fits the source models to the target domain under the supervision of pseudo target labels rather than matching data distributions. To collect high-quality initial pseudo target labels, our approach learns both specific and general source models to improve the generality of source models based on auxiliary learning. Going further, we introduce a class balanced coefficient of each category based on the number of samples to reduce the misclassification often caused by data imbalance. Experiments on real-world classification datasets show that the propsosed generally auxiliary training has a superiority over the baselines.

Transfer learning becomes an attractive technology to tackle a task from a target domain by leveraging previously acquired knowledge from a similar domain (source domain). Many existing transfer learning methods focus on learning one discriminator with single-source domain. Sometimes, knowledge from single-source domain might not be enough for predicting the target task. Thus, multiple source domains carrying richer transferable information are considered to complete the target task. Although there are some previous studies dealing with multi-source domain adaptation, these methods commonly combine source predictions by averaging source performances. Different source domains contain different transferable information; they may contribute differently to a target domain compared with each other. Hence, the source contribution should be taken into account when predicting a target task. In this article, we propose a novel multi-source contribution learning method for domain adaptation (MSCLDA). As proposed, the similarities and diversities of domains are learned simultaneously by extracting multi-view features. One view represents common features (similarities) among all domains. Other views represent different characteristics (diversities) in a target domain; each characteristic is expressed by features extracted in a source domain. Then multi-level distribution matching is employed to improve the transferability of latent features, aiming to reduce misclassification of boundary samples by maximizing discrepancy between different classes and minimizing discrepancy between the same classes. Concurrently, when completing a target task by combining source predictions, instead of averaging source predictions or weighting sources using normalized similarities, the original weights learned by normalizing similarities between source and target domains are adjusted using pseudo target labels to increase the disparities of weight values, which is desired to improve the performance of the final target predictor if the predictions of sources exist significant difference. Experiments on real-world visual data sets demonstrate the superiorities of our proposed method.

Unsupervised domain adaptation provides a variety of methods to leverage the previously gained knowledge from a labeled source domain to help complete a task from a similar unlabeled target domain. Many existing methods focus on transferring knowledge across single source and single target domains, while few studies deal with multi-source domain adaptation, which is more realistic and challengeable. Existing multi-source domain adaptation methods rarely consider the uncertainty of the transformed knowledge resulting from limited information in target domain. A fuzzy system allows imprecision and ambiguity within transfer, thus it can deal with problems with uncertainty. This work proposes a multi-source domain adaptation method with fuzzy-rule based deep neural networks (MDAFuz). The proposed method first extracts multi-view adapted features and pre-trains source classifiers. Using the learned features and classifiers, training samples are then split into multiple clusters, hence fuzzy rules can be built to learn new classifiers. At the same time, the cluster discriminator is trained to define the membership. Finally, by measuring the similarities among source and target domains using the pseudo target labels and a domain discriminator, the target task is completed by combining all source classifiers with regard to the learned weights. The experiment results on real-world visual datasets show the superiority of the proposed method.

Transfer learning is gaining increasing attention due to its ability to leverage previously acquired knowledge to assist in completing a prediction task in a similar domain. While many existing transfer learning methods deal with single source and single target problem without considering the fact that a target domain maybe similar to multiple source domains, this work proposes a multi-source domain adaptation method based on a deep neural network. Our method contains common feature extraction, specific predictor learning and target predictor estimation. Common feature extraction explores the relationship between source domains and target domain by distribution fusion and guarantees the strength of similar source domains during training, something which has not been well considered in existing works. Specific predictor learning trains source tasks with cross-domain distribution constraint and cross-domain predictor constraint to enhance the performance of single source. Target predictor estimation employs relationship extraction and selective strategy to improve the performance of the target task and to avoid negative transfer. Experiments on real-world visual datasets show the performance of the proposed method is superior to other deep learning baselines.