Multi-source Domain Adaptation Research Articles

Multi-source domain transfer network based on subdomain adaptation and minimum class confusion for EEG emotion recognition

Electroencephalogram (EEG) signals, which objectively reflect the state of the brain, are widely favored in emotion recognition research. However, the presence of cross-session and cross-subject variation in EEG signals has hindered the practical implementation of EEG-based emotion recognition technologies. In this article, we propose a multi-source domain transfer method based on subdomain adaptation and minimum class confusion (MS-SAMCC) in response to the addressed issue. First, we introduce the mix-up data augmentation technique to generate augmented samples. Next, we propose a minimum class confusion subdomain adaptation method (MCCSA) as a sub-module of the multi-source domain adaptation module. This approach enables global alignment between each source domain and the target domain, while also achieving alignment among individual subdomains within them. Additionally, we employ minimum class confusion (MCC) as a regularizer for this sub-module. We performed experiments on SEED, SEED IV, and FACED datasets. In the cross-subject experiments, our method achieved mean classification accuracies of 87.14% on SEED, 63.24% on SEED IV, and 42.07% on FACED. In the cross-session experiments, our approach obtained average classification accuracies of 94.20% on SEED and 71.66% on SEED IV. These results demonstrate that the MS-SAMCC approach proposed in this study can effectively address EEG-based emotion recognition tasks.

Unsupervised multi-source domain adaptation via contrastive learning for EEG classification

Individual differences in electroencephalography (EEG) present significant challenges for subject-independent EEG classification in brain–computer interfaces (BCIs). Existing domain adaptation methods often address individual differences by merging all source domains indistinguishably into a single source and aligning features between this aggregate source and the target domain. Neglecting the relationships between different source domains would hinder the model’s adaptability and generalization. Therefore, we propose a method called Contrastive Learning-based Unsupervised multi-source Domain Adaptation (CLUDA) for learning subject-independent representations in motor imagery. Our method not only aligns the conditional distributions of each source domain with the target domain but also reduces discrepancies among the source domains using contrastive learning, thus learning more generalized domain-invariant representations. Specifically, CLUDA effectively eliminates semantic variances by maximizing the similarity between positive pairs (same class) and minimizing the similarity between negative pairs (different classes) across subjects. Finally, we validated CLUDA on four motor imagery datasets and consistently achieved state-of-the-art performance.

Transfer graph feature alignment guided multi-source domain adaptation network for machinery fault diagnosis

In recent years, the application of unsupervised multi-source domain adaptation (MSDA) techniques for fault diagnosis has gained significant traction. Current research typically overlooks or fails to effectively capture critical data structure information during feature extraction. Another challenge is optimising the integration of information from multiple source domains to diagnose the target domain while avoiding negative transfer. To address these challenges, this study proposes a transfer graph feature alignment guided multi-source domain adaptation network (MDTGAL). In the proposed method, a transfer graph sample generator module (GSG) is constructed to model the data structure between source and target domains, and multiple graph feature extractors are employed to learn the data structure information from different domain combinations. A regularisation technique is introduced to extract the domain-invariant features by aligning the parameters across multiple independent graph feature extractors. In addition, a weighted soft-voting mechanism based on the polynomial kernel-induced maximum mean difference metric (PK-MMD) is designed to fuse the outputs from multiple classifiers, to comprehensively account for the influence of each source domain. The proposed method was tested on multi-source domain transfer tasks involving various operating conditions of rotating machinery. The experimental results demonstrate that the MDTGAL exhibits superior cross-domain diagnostic performance, outperforming existing mainstream methods. In addition, this study explored the impact of varying numbers of source domains on the diagnostic accuracy of the target domain, providing insights into selecting the correct number of source domains for specific MSDA tasks.

USMDA: Unsupervised Multisource Domain Adaptive ADHD prediction model using neuroimaging

There is an increasing number of large-scale cross-site database collections of neuroimaging markers (sMRI and fMRI) for studying neurodevelopmental illnesses (NDDs). Although a huge amount of data favors machine learning-based categorization algorithms, the unique heterogeneity of each site can impair cross-site generalization capacity. It is critical to create Unsupervised domain adaption methods for NDDs because obtaining appropriate diagnoses or labeling for NDDs might be problematic. In our work, we focus on Attention-deficit/hyperactivity disorder, which is the most common and frequently co-occurring NDD. We present an unsupervised multisource domain adaptation network (USMDA) with four primary components: Domain Alignment Module, Discrepancy Estimator, Pre-trained Model Generator, and Unsupervised Network. The Domain Alignment module is intended to incrementally and effectively align graph representations of the source and target domains. At the same time, the binary cross entropy regularizer is introduced for the first time during the training of a model learned on multiple source domains to improve existing feature alignment methods such as Transfer Joint Matching (TJM) and Joint Distribution Adaptation (JDA) by learning good unsupervised features. In an unsupervised network, the grid search optimization technique generates the optimal pseudo labels for unlabeled target data. We validate our proposed technique first on existing feature-level DA methods such as JDA, TJM, and Correlation alignment (CORAL), on the publically accessible dataset ADHD-200 and then by using binary cross-entropy in existing DA methods such as TJMCE and JDACE. The experimental results show that our proposed USMDAJDACE method when applied to multisite sMRI and fMRI ADHD data, can significantly outperform competitive methods for multi-center ADHD diagnosis.

MSS-JDA: Multi-source self-selected joint domain adaptation method based on cross-subject EEG emotion recognition

The electroencephalogram (EEG) is widely used to identify different emotional states. However, cross-subject emotion recognition based on EEG data is challenging due to physiological differences among participants. Many studies have proposed using multi-source domain adaptation methods to address cross-subject issues and have achieved promising results. multi-source domain adaptation involves constructing independent branches for EEG data from multiple subjects, using one-to-one domain adaptation, and extracting domain-specific features, followed by reasoning through multiple branches. However, a current practical obstacle is the increasing computational time cost as the number of source domains grows. Moreover, too many source domains may lead to domain shift issues. To address these challenges, we propose a novel emotion recognition method based on Multi-Source Domain Branch Self-Selected Joint Domain Adaptation (MSS-JDA). Initially, we construct a shared feature extractor for multiple source domain branches to extract common low-level features. Next, we use a one-to-one associated joint adaptation method to extract features specific to each domain. During training, our method of multi-source domain branch self-selection, guided by prior knowledge in the early training phase, prunes source domain branches that deviate significantly from the target domain, retaining only a certain number of source domain branches close to the target domain for continued training. Finally, domain-specific features are input into their respective classifiers to obtain N predictions, and their average is taken as the final result. Extensive experiments on the SEED and SEED-IV datasets demonstrate that the MSS-JDA model achieves accuracy and standard deviation scores of 93.78%, 3.39 and 78.93%, 7.39 respectively, surpassing most existing models. The experiments indicate that the proposed MSS-JDA method improves the accuracy of EEG emotion recognition classification, thus benefiting practical applications in EEG classification.

Towards Recognizing Food Types for Unseen Subjects

Recognizing food types through sensor signals for unseen users remains remarkably challenging, despite extensive recent studies. The efficacy of prior machine learning techniques is dwarfed by giant variations of data collected from multiple participants, partly because users have varied chewing habits and wear sensor devices in various manners. This work treats the problem as an instance of the domain adaptation problem, where each user represents a domain. We develop the first multi-source domain adaptation (MSDA) method for food-typing recognition, which consists of three major components: stratified normalization, a multi-source domain adaptor, and adaptive ensemble learning. New techniques are developed for each component. Using a real-world dataset comprised of 15 participants, we demonstrate that our method achieves \(1.33\times\) to \(2.13\times\) improvement in accuracy compared with nine state-of-the-art MSDA baselines. Additionally, we perform an in-depth ablation study to examine the behavior of each component and confirm their efficacy.

MURDA: Multisource Unsupervised Raman Spectroscopy Domain Adaptation Model with Reconstructed Target Domains for Medical Diagnosis Assistance.

Artificial intelligence combined with Raman spectroscopy for disease diagnosis is on the rise. However, these methods require a large amount of annotated spectral data for modeling to achieve high diagnostic accuracy. Annotating labels consumes significant medical resources and time. To reduce dependence on labeled medical data resources, we propose a method called Multisource Unsupervised Raman Spectroscopy Domain Adaptation Model with Reconstructed Target Domains (MURDA). It transfers knowledge learned from source domain data sets of different diseases to an unlabeled target domain data set. Compared to knowledge transfer from a single source domain, knowledge from multiple disease source domains provides more generalized knowledge. Considering the diversity of autoimmune diseases and the limited sample size, we apply MURDA to assist in the medical diagnosis of autoimmune diseases. Additionally, we propose a Double-Branch Multiscale Convolutional Self-Attention (DMCS) feature extractor that is more suitable for spectral data feature extraction. On three sets of serum Raman spectroscopy data sets for autoimmune diseases, the multisource domain adaptation diagnostic accuracy of MURDA was superior to traditional single source and multisource models, with accuracy rates of 73.6%, 83.4%, and 82.9%, respectively. Compared with pure source tasks without domain adaptation, it improved by 15.1%, 36%, and 21.6%, respectively. This demonstrates the effectiveness of Raman spectroscopy combined with MURDA in diagnosing autoimmune diseases. We investigated the important decision dependency peaks in migration tasks, providing assistance for future research on artificial intelligence combined with Raman spectroscopy for diagnosing autoimmune diseases. Furthermore, to validate the effectiveness and generalization performance of MURDA, we conducted experiments on the publicly available RRUFF data set, exploring the application of multisource unsupervised domain adaptation in more Raman spectroscopy scenarios.

Multi-source domain adaptation network for partial discharge severity assessment in gas-insulated switchgear

Abstract Gas-insulated switchgear (GIS) partial discharge (PD) severity assessment is critical for ensuring the reliable operation of GIS systems. However, existing assessment methods often overlook the long-term dependencies of historical data and fail to adequately address challenges related to limited on-site samples and variations in sample distribution. To overcome these challenges, we propose a novel multi-source domain adaptation network (MSDAN) specifically designed for on-site GIS PD severity assessment, which is the first model developed considering distribution differences in GIS PD severity assessment for different defect types. Our approach begins with the development of a feature extractor that captures both discernible PD features and long-term dependencies. We then introduce a multi-source domain adaptation strategy to mitigate distribution disparities across PD severity samples from different defect types, ensuring effective domain alignment. Additionally, we incorporate an adaptive weighted classification mechanism to accurately assess PD severity by considering the varying contributions of different defect types to the target domain task. Experimental results demonstrate that MSDAN achieves a remarkable accuracy of 95.38% in on-site GIS PD severity assessment, outperforming other benchmark models in both accuracy and robustness. This highlights the potential of MSDAN as a robust solution for real-world GIS PD severity assessment.

Multi-source domain adaptation for dependency parsing via domain-aware feature generation

Multi-source domain adaptation for dependency parsing via domain-aware feature generation

Multi-source domain adaptation using diffusion denoising for bearing fault diagnosis under variable working conditions

Transfer learning of multi-source domain adaptation seems a promising way for fault diagnosis of roller element bearings under variable working conditions. Data imbalance affects the performance of multi-source domain adaptation greatly and is expected to be solved by GAN. However, GAN-based transfer learning diagnosis models suffer pattern collapse and training instability, leading to unsatisfying diagnosis results in practical engineering. This paper proposes a denoising diffusion multi-source domain adaptation model (DDMDA). The proposed model uses diffusion denoising, which has better performance and is simpler to train than GAN, to generate shifted source domains for solving the data imbalance problem. A new noise prediction structure in diffusion denoising named Utrans-net, is constructed to restore the data distribution in the shifted source domain. Also, a multiple-domain discriminator structure is designed to extract features from multiple source domains to solve the issue of variable working conditions. Advanced models are used in this paper to compare with the proposed model for validation. Experimental demonstrations show that the proposed model is superior to the comparison models with satisfying performance.

A general multi-source ensemble transfer learning framework for health prognostic of lithium-ion batteries

This paper proposes a novel multi-source domain adaptation method for the health prediction in the absence of target batteries labels. First, a computationally efficient and effective source domain selection method is proposed. The proposed source domain selection method selects source domains with excellent transferability based on Euclidean distance and Sample Entropy thresholds, thereby improving the performance of multi-source domain adaptation. Second, improved maximum mean discrepancy is proposed to guide the representation alignment. Compared with the original maximum mean discrepancy, it matches the source data with the target data in the order of aging, thus preserving the time order of the distribution. Third, we develop a multi-source domain adaptation network that contains a domain-invariant feature generator and several domain-specific feature generators and estimators. Two out of 12 datasets are used as target LIBs to evaluate the performance of the proposed method. The mean absolute error of the proposed method on these two datasets is only 0.671% and 0.868%, respectively. While the maximum error of MDA-IM-GC on both datasets does not exceed 5.2%. The experiment results indicate that the proposed network provides impressive accuracy with only 1/10 of unlabeled target historical data accessible and outperforms the single-source domain adaptation approach.

Towards Discriminability with Distribution Discrepancy Constrains for Multisource Domain Adaptation

Multisource domain adaptation (MDA) is committed to mining and extracting data concerning target tasks from several source domains. Many recent studies have focused on extracting domain-invariant features to eliminate domain distribution differences. However, there are three aspects that require further consideration. (1) Efforts should be made to ensure the maximum correlation in the potential subspace between the source and target domains. (2) While aligning the marginal distribution, the conditional distribution must also be considered. (3) Merely aligning the source distribution and target distribution cannot guarantee sufficient differentiation for classification tasks. To address these problems, we propose a novel approach named towards discriminability with distribution discrepancy constrains for multisource domain adaptation (TD-DDC). Specifically, TD-DDC first mines features of maximal relations learned from all domains while constructing domain data distribution mean distance metrics for interdomain distribution adaptation. Simultaneously, we integrate discriminability into domain alignment, which means increasing the distance among labels that are distinct from one another while reducing the distance among labels that are the same. Our proposed method not only reduces the interdomain distributional differences but also takes into account the preservation of interdomain correlation and inter-category discrimination. Numerous experiments have shown that TD-DDC performs much better than its competitors on three visual benchmark test databases.

TSMDA: intelligent fault diagnosis of rolling bearing with two stage multi-source domain adaptation

TSMDA: intelligent fault diagnosis of rolling bearing with two stage multi-source domain adaptation

Machining quality prediction of multi-feature parts using integrated multi-source domain dynamic adaptive transfer learning

Machining quality prediction of multi-feature parts has been a challenging problem because of small dataset and inconsistent quality data distribution with respect to each machining feature. Transfer learning that leverages knowledge of one task and can be repurposed on another task seems a good solution for this purpose. However, traditional transfer learning typically has a single source domain and a target domain, which limits its applications in multi-source scenarios (e.g., multi-feature). To solve this issue, this paper proposes a novel integrated multi-source domain dynamic adaptive transfer learning (IMD-DATL) framework for machining quality prediction of multi-feature part machining systems. Specifically, a domain-sample similarity double matching multi-source domain integration method is designed to construct the integration knowledge transfer from multiple source domains to the target domain. A residual feature extraction network based on sample entropy-dynamic channel double-layer attention structure and a fine-grained transferable feature attention module are designed. These three attentions are used to improve the feature learning ability and the adaptation level to the predicted object in the three dimensions of sample, channel and data feature. Finally, multiple sets of comparative experiments in thin-walled part machining systems confirm the effectiveness and superiority of the proposed method for cross-domain quality prediction. Compared with other traditional transfer learning methods, the MAE, RMSE and Score on average of this method are increased by 5.47 %, 4.59 % and 4.84 %, respectively, compared with other multi-source domain adaptation methods, the MAE, RMSE and Score on average of this method are increased by 7.13 %, 7.37 % and 6.52 %, respectively.

Domain Discrimination Expert Weighted Network for Multi-Source Carotid Artery Plaque Classification

The rupture of unstable plaques is a major cause of acute cardiovascular events. The early assessment of carotid plaques can significantly reduce the cardiovascular risks, so developing evaluation models suitable for data from different centers is of great clinical importance. This study leverages plaque datasets from multiple centers to develop a Weighted multi-source carotid artery plaque Unsupervised Classification Framework (WUCF). The multi-source domain adaptation module of the WUCF focuses on maintaining feature consistency between each independent source and target center, while also integrating a specialized domain discriminator expert. This ensures that the knowledge from each source center is effectively learned and combined for accurate predictions in the target domain. The experimental evaluation of WUCF, using datasets from three centers, has demonstrated the method’s superiority and robustness.

Multi-Source Domain Adaptation Enhanced Warehouse Dwell Time Prediction

Multi-Source Domain Adaptation Enhanced Warehouse Dwell Time Prediction

Active Dynamic Weighting for multi-domain adaptation

Multi-source unsupervised domain adaptation aims to transfer knowledge from multiple labeled source domains to an unlabeled target domain. Existing methods either seek a mixture of distributions across various domains or combine multiple single-source models for weighted fusion in the decision process, with little insight into the distributional discrepancy between different source domains and the target domain. Considering the discrepancies in global and local feature distributions between different domains and the complexity of obtaining category boundaries across domains, this paper proposes a novel Active Dynamic Weighting (ADW) for multi-source domain adaptation. Specifically, to effectively utilize the locally advantageous features in the source domains, ADW designs a multi-source dynamic adjustment mechanism during the training process to dynamically control the degree of feature alignment between each source and target domain in the training batch. In addition, to ensure the cross-domain categories can be distinguished, ADW devises a dynamic boundary loss to guide the model to focus on the hard samples near the decision boundary, which enhances the clarity of the decision boundary and improves the model’s classification ability. Meanwhile, ADW applies active learning to multi-source unsupervised domain adaptation for the first time, guided by dynamic boundary loss, proposes an efficient importance sampling strategy to select target domain hard samples to annotate at a minimal annotation budget, integrates it into the training process, and further refines the domain alignment at the category level. Experiments on various benchmark datasets consistently demonstrate the superiority of our method.

Multi-source domain adaptation handling inaccurate label spaces

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.

Multisource Domain Adaptation With Interval-Valued Target Data via Fuzzy Neural Networks

Multisource Domain Adaptation With Interval-Valued Target Data via Fuzzy Neural Networks

Graph correlated discriminant embedding for multi-source domain adaptation

As a main branch of domain adaptation (DA), multi-source DA (MSDA) has attracted increasing attention for exploiting information from multi-source domain data. However, how to effectively explore useful information from each source domain for target tasks is still a key problem. In this paper, to fully explore multiple information of different domain data, we propose a graph correlated discriminant embedding (GCDE) method for MSDA. In GCDE, the category-discriminative information, manifold structure, and correlation learning are fully considered. Specifically, GCDE encodes the within- and between- class information of each domain data, preserves the local and global structure information of the data, and extracts the maximization correlative features from different domains by designing a novel correlative learning scheme. We also extend GCDE to a nonlinear case and obtain kernel GCDE (KGCDE). We have conducted extensive experiments on four public data benchmarks to verify the performance of GCDE and KGCDE. The promising performance on the databases prove the efficiency of our methods with the comparison of the advanced approaches.