Class-aware multi-source domain adaptation algorithm for medical image analysis using reweighted matrix matching strategy

Unsupervised domain adaptation aims to transfer knowledge from labeled source domain to unlabeled target domain. Recently, multisource domain adaptation (MDA) has begun to attract attention. Its performance should go beyond simply mixing all source domains together for knowledge transfer. In this article, we propose a novel prototype-based method for MDA. Specifically, for solving the problem that the target domain has no label, we use the prototype to transfer the semantic category information from source domains to target domain. First, a feature extraction network is applied to both source and target domains to obtain the extracted features from which the domain-invariant features and domain-specific features will be disentangled. Then, based on these two kinds of features, the named inherent class prototypes and domain prototypes are estimated, respectively. Then a prototype mapping to the extracted feature space is learned in the feature reconstruction process. Thus, the class prototypes for all source and target domains can be constructed in the extracted feature space based on the previous domain prototypes and inherent class prototypes. By forcing the extracted features are close to the corresponding class prototypes for all domains, the feature extraction network is progressively adjusted. In the end, the inherent class prototypes are used as a classifier in the target domain. Our contribution is that through the inherent class prototypes and domain prototypes, the semantic category information from source domains is transformed into the target domain by constructing the corresponding class prototypes. In our method, all source and target domains are aligned twice at the feature level for better domain-invariant features and more closer features to the class prototypes, respectively. Several experiments on public data sets also prove the effectiveness of our method.

Dual contrastive universal adaptation network for multi-source visual recognition

Multisource unsupervised domain adaptation (MUDA) is an important and challenging topic for target classification with the assistance of labeled data in source domains. When we have several labeled source domains, it is difficult to map all source domains and target domain into a common feature space for classifying the targets well. In this article, a new progressive multisource domain adaptation network (PMSDAN) is proposed to further improve the classification performance. PMSDAN mainly consists of two steps for distribution alignment. First, the multiple source domains are integrated as one auxiliary domain to match the distribution with the target domain. By doing this, we can generally reduce the distribution discrepancy between each source and target domains, as well as the discrepancy between different source domains. It can efficiently explore useful knowledge from the integrated source domain. Second, to mine assistance knowledge from each source domain as much as possible, the distribution of the target domain is separately aligned with that of each source domain. A weighted fusion method is employed to combine the multiple classification results for making the final decision. In the optimization of domain adaption, weighted hybrid maximum mean discrepancy (WHMMD) is proposed, and it considers both the interclass and intraclass discrepancies. The effectiveness of the proposed PMSDAN is demonstrated in the experiments comparing with some state-of-the-art methods.

Multi-source Domain Adaptation (MSDA) is more practical but challenging than the conventional unsupervised domain adaptation due to the involvement of diverse multiple data sources. Two fundamental challenges of MSDA are: (i) how to deal with the diversity in the multiple source domains and (ii) how to cope with the data shift between the target domain and the source domains. In this paper, to address the first challenge, we propose a theoretical-guaranteed approach to combine domain experts locally trained on its own source domain to achieve a combined multi-source teacher that globally predicts well on the mixture of source domains. To address the second challenge, we propose to bridge the gap between the target domain and the mixture of source domains in the latent space via a generator or feature extractor. Together with bridging the gap in the latent space, we train a student to mimic the predictions of the teacher expert on both source and target examples. In addition, our approach is guaranteed with rigorous theory offered insightful justifications of how each component influences the transferring performance. Extensive experiments conducted on three benchmark datasets show that our proposed method achieves state-of-the-art performances to the best of our knowledge.

Multi-source domain adaptation method for textual emotion classification using deep and broad learning

Domain adaptation has achieved promising results in many areas, such as image classification and object recognition. Although a lot of algorithms have been proposed to solve the task with different domain distributions, it remains a challenge for multi-source unsupervised domain adaptation. In addition, most of the existing algorithms learn a classifier on the source domain and predict the labels for the target data, which indicates that only the knowledge derived from the hyperplane is transferred to the target domain and the structure information is ignored. In light of this, we propose a novel algorithm for multi-source unsupervised domain adaptation. Generally speaking, we aim to preserve the whole structure from source domains and transfer it to serve the task on the target domain. The source and target data are put together for clustering, which simultaneously explores the structures of the source and target domains. The structure-preserved information from source domain further guides the clustering process on the target domain. Extensive experiments on two widely used databases on object recognition and face identification show the substantial improvement of our proposed approach over several state-of-the-art methods. Especially, our algorithm can take use of multi-source domains and achieve robust and better performance compared with the single source domain adaptation methods.

Regular domain adaptation (DA) problems are interested in source examples drawn from a single source distribution, yet they probably come from multiple source domains in reality. Compared with DAs, Multi-Source DA (MSDA) is more challenging to settle: The extra domain shifts exist between source domains and moreover, the multi-source domains may also disagree on their semantic information. In this section, we surveyed Deep CockTail Network (DCTN), a prevalent MSDA algorithm that battles the multi-source-derived domain and semantic shifts. The ideology behind is inspired by making cocktails with multiple kinds of stuff (i.e. sources in our background). In particular, DCTN replays two alternating learning phases: (1) DCTN goes through a multi-way adversarial DA process to minimize the domain discrepancy between the target and each source, in order to obtain domain-invariant features. In this process, each target example would lead to the source-specific perplexity scores, denoting how similar each target feature appears to a feature from one of the source domains. (2) Integrated with the perplexity scores, the multi-source category classifiers categorizes target samples, and the pseudo-labeled target samples and source samples jointly update the category classifiers and the feature extractor. In the empirical studies, DCTNs are evaluated in three domain adaptation benchmarks in vanilla and source-category-shift MSDA scenarios. The results thoroughly evidence the superiority of DCTN framework that resists negative transfers across domains and tasks.

Traditional cross-domain transfer learning faces significant challenges in rotating machinery fault diagnosis due to the general scarcity of fault samples. Furthermore, under complex operating conditions, differences in data distribution between the source domain and target domain can also cause negative migration. To overcome these limitations, this paper proposes a Multi-Source Contrastive Learning Domain Adaptation (MSCLDA) method. Firstly, the MSCLDA approach employs a Wasserstein Generative Adversarial Network (WGAN) to synthesize minority-class fault samples. Subsequently, it integrates a Supervised Contrastive Learning (SCL) strategy to optimize feature consistency between real and synthetic samples, thereby effectively mitigating the training bias caused by category imbalance. Furthermore, we devise a prototype-based multi-pseudo label self-correction mechanism. By incorporating this mechanism with a multiple pseudo-label-guided local maximum mean discrepancy (MP-LMMD) strategy, we achieve precise alignment of feature distributions between the source domains and target subdomains. Finally, an adaptive weighting mechanism is introduced to assign higher weights to source domains more relevant to the target domain, thereby reducing the adverse impact of less relevant domains and mitigating negative transfer. Cross-domain experiments on current signals from industrial robots and vibration signals from bearings show that MSCLDA achieves superior fault identification performance under various working conditions, with an average accuracy of up to 99.05%, and effectively suppresses negative transfer.

Intelligent fault diagnosis methods based on domain adaptation (DA) have been extensively employed for tackling domain shift problems, and the basic diagnosis tasks under time-varying working conditions were well achieved. Nevertheless, the existing methods usually focus on learning diagnosis knowledge from single-source domain while ignoring abundant underlying information in multisource domain. In practical scenarios, multiple source domains are available, and there are few studies in fault diagnosis based on multisource DA. To this end, a novel multisource domain feature adaptation network (MDFAN) is proposed for bearing fault diagnosis under time-varying working conditions. The proposed network first uses a feature extractor to learn transferable features from different pairs of source and target domains, and then, a domain-specific distribution alignment module is constructed, which adopts the intra-domain alignment strategy and the inter-domain alignment strategy to reduce the shift between all domain pairs. Meanwhile, considering the classifier prediction disagreement, a classifier alignment module is further designed to relieve the classifier prediction discrepancy and enhance the prediction consistency. Case studies on two real datasets with multiple sources demonstrate the effectiveness of the proposed network, and the comparison results show its robustness and superiority.

This paper addresses the problem of land-cover map updating by classification of multitemporal remote-sensing images in the context of domain adaptation (DA). The basic assumptions behind the proposed approach are twofold. The first one is that training data (ground reference information) are available for one of the considered multitemporal acquisitions (source domain) whereas they are not for the other (target domain). The second one is that multitemporal acquisitions (i.e., target and source domains) may be characterized by different sets of classes. Unlike other approaches available in the literature, the proposed DA Bayesian classifier based on maximum a posteriori decision rule (DA-MAP) automatically identifies whether there exist differences between the set of classes in the target and source domains and properly handles these differences in the updating process. The proposed method was tested in different scenarios of increasing complexity related to multitemporal image classification. Experimental results on medium-resolution and very high resolution multitemporal remote-sensing data sets confirm the effectiveness and the reliability of the proposed DA-MAP classifier.

Fault diagnosis plays a critical role in ensuring the safe operation of machinery. Multi-source domain adaptation (DA) leverages rich fault knowledge from source domains to enhance diagnostic performance on unlabeled target domains. However, most existing methods only align marginal distributions, neglecting inter-class relationships, which results in decreased performance under variable working conditions and small samples. To overcome these limitations, two stage multi-source domain adaptation (TSMDA) has been proposed for bearing fault diagnosis. Specifically, wavelet packet decomposition is applied to analyze fault information from signals. For small sample datasets, Diffusion is used to augment the dataset and serve as the source domain. Next, multi-scale features are extracted, and mutual information is computed to prevent the negative transfer. DA is divided into two stages. Firstly, multikernel maximum mean discrepancy is used to align the marginal distributions of the multi-source and target domains. Secondly, the target domain is split into subdomains based on the calculated pseudo-labels. Conditional distributions are aligned by minimizing the distance from samples to the center of the non-corresponding domain. The effectiveness of the proposed method is verified by extensive experiments on two public datasets and one experimental dataset. The results demonstrate that TSMDA has high and stable diagnostic performance and provides an effective method for practical fault diagnosis.

In the absence of the labeled samples in a domain referred to as target domain, Domain Adaptation (DA) techniques come in handy. Generally, DA techniques assume there are available source domains that share similar predictive function with the target domain. Two core challenges of DA typically arise, variance that exists between source and target domains, and the inherent source hypothesis bias. In this paper, we first propose a Stability Transfer criterion for selecting relevant source domains with low variance. With this criterion, we introduce a TARget learning Assisted by Source Classifier Adaptation (TARASCA) method to address the two core challenges that have impeded the performances of DA techniques. To verify the robustness of TARASCA, extensive experimental studies are carried out with comparison to several state-of-the-art DA methods on the real-world Sentiment and Newsgroups datasets, where various settings for the class ratios of the source and target domains are considered.

In many real-world applications, it may be desirable to benefit from a classi-fier trained on a given source task from some largely annotated dataset in order to address a different but related target task for which only weakly labeled data are available. Domain adaptation (DA) is the framework which aims at leveraging the statistical similarities between the source and target distributions to learn well. Current theoretical results show that the efficiency of DA algorithms depends on (i) their capacity of minimizing the divergence between the source and target domains and (ii) the existence of a good hypothesis that commits few errors in both domains. While most of the work in DA has focused on new divergence measures, the second aspect, often modeled as the capability term, remains surprisingly under investigated. In this paper, we show that the problem of the best joint hypothesis estimation can be reformulated using a Wasserstein distance-based error function in the context of multi-source DA. Based on this idea, we provide a theoretical analysis of the capability term and derive inequalities allowing us to estimate it from finite samples. We empirically illustrate the proposed idea on different data sets.

Multi-source domain adaptation (MSDA) plays an important role in industrial model generalization. Recent efforts regarding MSDA focus on enhancing multi-domain distributional alignment while omitting three issues, e.g., the class-level discrepancy quantification, the unavailability of noisy pseudo labels, and source transferability discrimination, potentially resulting in suboptimal adaption performance. Therefore, we address these issues by proposing a prototype aggregation method that models the discrepancy between source and target domains at the class and domain levels. Our method achieves domain adaptation based on a group of prototypes (i.e., representative feature embeddings). A similarity score-based strategy is designed to quantify the transferability of each domain. At the class level, our method quantifies class-specific cross-domain discrepancy according to reliable target pseudo labels. At the domain level, our method establishes distributional alignment between noisy pseudo-labeled target samples and the source domain prototypes. Therefore, adaptation at the class and domain levels establishes a complementary mechanism to obtain accurate predictions. The results on three standard benchmarks demonstrate that our method outperforms most state-of-the-art methods. In addition, we provide further elaboration of the proposed method in light of the interpretable results obtained from the analysis experiments.

In many real-world applications, the domain of model learning (referred as source domain) is usually inconsistent with or even different from the domain of testing (referred as target domain), which makes the learnt model degenerate in target domain, i.e., the test domain. To alleviate the discrepancy between source and target domains, we propose a domain adaptation method, named as Bi-shifting Auto-Encoder network (BAE). The proposed BAE attempts to shift source domain samples to target domain, and also shift the target domain samples to source domain. The non-linear transformation of BAE ensures the feasibility of shifting between domains, and the distribution consistency between the shifted domain and the desirable domain is constrained by sparse reconstruction between them. As a result, the shifted source domain is supervised and follows similar distribution as target domain. Therefore, any supervised method can be applied on the shifted source domain to train a classifier for classification in target domain. The proposed method is evaluated on three domain adaptation scenarios of face recognition, i.e., domain adaptation across view angle, ethnicity, and imaging sensor, and the promising results demonstrate that our proposed BAE can shift samples between domains and thus effectively deal with the domain discrepancy.