Multi-source Domain Adaptation Research Articles

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.

Optimization-based multi-source transfer learning for modeling of nonlinear processes

This work develops an optimization-based multi-source transfer learning scheme for modeling of nonlinear chemical processes. Specifically, a transfer learning neural network model for a target process with limited data is developed using the pre-trained model obtained with multiple source processes. Since the performance of transfer learning models depends on the quality of the pre-trained models, we propose a novel Bayesian optimization problem to optimize the selection of multi-source data for the pre-trained models by first deriving a generalization error bound for multi-source domain adaptation using Y-discrepancy distance. Subsequently, the optimization problem is formulated using the theoretical error bound to select the optimal set of multiple sources, which can provide a good initial guess of the weight parameters for transfer learning model. Finally, a simulation study of a chemical reactor process in Aspen Plus Dynamics is conducted to illustrate the effectiveness of the optimization-based multi-source transfer learning scheme.

Multi-Source Domain Adaptation for Medical Image Segmentation.

Unsupervised domain adaptation(UDA) aims to mitigate the performance drop of models tested on the target domain, due to the domain shift from the target to sources. Most UDA segmentation methods focus on the scenario of solely single source domain. However, in practical situations data with gold standard could be available from multiple sources (domains), and the multi-source training data could provide more information for knowledge transfer. How to utilize them to achieve better domain adaptation yet remains to be further explored. This work investigates multi-source UDA and proposes a new framework for medical image segmentation. Firstly, we employ a multi-level adversarial learning scheme to adapt features at different levels between each of the source domains and the target, to improve the segmentation performance. Then, we propose a multi-model consistency loss to transfer the learned multi-source knowledge to the target domain simultaneously. Finally, we validated the proposed framework on two applications, i.e., multi-modality cardiac segmentation and cross-modality liver segmentation. The results showed our method delivered promising performance and compared favorably to state-of-the-art approaches.

Cycle Self-Refinement for Multi-Source Domain Adaptation

Multi-source domain adaptation (MSDA) aims to transfer knowledge from multiple source domains to the unlabeled target domain. In this paper, we propose a cycle self-refinement domain adaptation method, which progressively attempts to learn the dominant transferable knowledge in each source domain in a cycle manner. Specifically, several source-specific networks and a domain-ensemble network are adopted in the proposed method. The source-specific networks are adopted to provide the dominant transferable knowledge in each source domain for instance-level ensemble on predictions of the samples in target domain. Then these samples with high-confidence ensemble predictions are adopted to refine the domain-ensemble network. Meanwhile, to guide each source-specific network to learn more dominant transferable knowledge, we force the features of the target domain from the domain-ensemble network and the features of each source domain from the corresponding source-specific network to be aligned with their predictions from the corresponding networks. Thus the adaptation ability of source-specific networks and the domain-ensemble network can be improved progressively. Extensive experiments on Office-31, Office-Home and DomainNet show that the proposed method outperforms the state-of-the-art methods for most tasks.

Non-Uniformly Weighted Multisource Domain Adaptation Network For Fault Diagnosis Under Varying Working Conditions

Most transfer learning-based fault diagnosis methods learn diagnostic information from the source domain to enhance performance in the target domain. However, in practical applications, usually there are multiple available source domains, and relying on diagnostic information from only a single source domain limits the transfer performance. To this end, a non-uniformly weighted multisource domain adaptation network is proposed to address the above challenge. In the proposed method, an intra-domain distribution alignment strategy is designed to eliminate multi-domain shifts and align each pair of source and target domains. Furthermore, a non-uniform weighting scheme is proposed for measuring the importance of different sources based on the similarity between the source and target domains. On this basis, a weighted multisource domain adversarial framework is designed to enhance multisource domain adaptation performance. Numerous experimental results on three datasets validate the effectiveness and superiority of the proposed method.

Graphical Modeling for Multi-Source Domain Adaptation.

Multi-Source Domain Adaptation (MSDA) focuses on transferring the knowledge from multiple source domains to the target domain, which is a more practical and challenging problem compared to the conventional single-source domain adaptation. In this problem, it is essential to model multiple source domains and target domain jointly, and an effective domain combination scheme is also highly required. The graphical structure among different domains is useful to tackle these challenges, in which the interdependency among various instances/categories can be effectively modeled. In this work, we propose two types of graphical models, i.e. Conditional Random Field for MSDA (CRF-MSDA) and Markov Random Field for MSDA (MRF-MSDA), for cross-domain joint modeling and learnable domain combination. In a nutshell, given an observation set composed of a query sample and the semantic prototypes (i.e. representative category embeddings) on various domains, the CRF-MSDA model seeks to learn the joint distribution of labels conditioned on the observations. We attain this goal by constructing a relational graph over all observations and conducting local message passing on it. By comparison, MRF-MSDA aims to model the joint distribution of observations over different Markov networks via an energy-based formulation, and it can naturally perform label prediction by summing the joint likelihoods over several specific networks. Compared to the CRF-MSDA counterpart, the MRF-MSDA model is more expressive and possesses lower computational cost. We evaluate these two models on four standard benchmark data sets of MSDA with distinct domain shift and data complexity, and both models achieve superior performance over existing methods on all benchmarks. In addition, the analytical studies illustrate the effect of different model components and provide insights about how the cross-domain joint modeling performs.

Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy.

Determining causes of deaths (CODs) occurred outside of civil registration and vital statistics systems is challenging. A technique called verbal autopsy (VA) is widely adopted to gather information on deaths in practice. A VA consists of interviewing relatives of a deceased person about symptoms of the deceased in the period leading to the death, often resulting in multivariate binary responses. While statistical methods have been devised for estimating the cause-specific mortality fractions (CSMFs) for a study population, continued expansion of VA to new populations (or "domains") necessitates approaches that recognize between-domain differences while capitalizing on potential similarities. In this article, we propose such a domain-adaptive method that integrates external between-domain similarity information encoded by a prespecified rooted weighted tree. Given a cause, we use latent class models to characterize the conditional distributions of the responses that may vary by domain. We specify a logistic stick-breaking Gaussian diffusion process prior along the tree for class mixing weights with node-specific spike-and-slab priors to pool information between the domains in a data-driven way. The posterior inference is conducted via a scalable variational Bayes algorithm. Simulation studies show that the domain adaptation enabled by the proposed method improves CSMF estimation and individual COD assignment. We also illustrate and evaluate the method using a validation dataset. The article concludes with a discussion of limitations and future directions.

A universal multi-source domain adaptation method with unsupervised clustering for mechanical fault diagnosis under incomplete data

Recently, due to the difficulty of collecting condition data covering all mechanical fault types in industrial scenarios, the fault diagnosis problem under incomplete data is receiving increasing attention where no target prior information can be available. The existing open-set or universal domain adaptation (DA) diagnosis methods typically treat private fault samples in the target as a generalized “unknown” fault class, neglecting their inherent structure. This oversight can lead to confusion in latent feature space representations and difficulties in separating unknown samples. Therefore, a universal DA method with unsupervised clustering is developed to explore the intrinsic structure of the target samples for mechanical fault diagnosis, where multi-source information on different working conditions is considered to transfer complementary knowledge. First, a composite clustering metric combining single-domain and cross-domain evaluation is constructed to recognize shared and unknown health classes on source–target domains. Second, to alleviate the intra-class shift while enlarging the inter-class gap, a class-wise DA algorithm is suggested which operates on the basis of maximum mean discrepancy. Finally, an entropy regularization criterion is utilized to facilitate clustering of different health classes. The efficacy of the presented approach in the fault diagnosis issues when monitoring data is inadequate has been verified through extensive experiments on three rotating machinery datasets.

Pose-robust personalized facial expression recognition through unsupervised multi-source domain adaptation

Pose-robust personalized facial expression recognition is rather challenging, as facial expressions are subject-related and pose-dependent. Multi-source domain adaptation tries to leverage knowledge from multiple source domains to boost the performance of the target domain. For pose-robust personalized facial expression recognition, the images of the source domain are from multiple sources since the images are under different poses. Thus, in this paper, we propose a novel unsupervised multi-source domain adaptation framework for pose-robust personalized facial expression recognition. The proposed framework consists of five components: a source encoder, a target encoder, an expression classifier, a view discriminator, and a domain discriminator. The source encoder and target encoder learn facial representations from facial images in the training and testing sets, respectively. The expression classifier recognizes expressions from the learned representations. The view discriminator classifies poses. The domain discriminator distinguishes the learned representations of the source domain from those of the target domain. The source encoder works cooperatively with the expression classifier and adversarially with the view discriminator. The target encoder aims to learn domain robust representations and fool the domain discriminator, while the domain discriminator tries to distinguish between the source and target domains. Through adversarial learning, the distribution of the learned representations from the source domain converges to that from the target domain. Thus, the feature representation extracted by the target encoder is pose-invariant and target subject-specific. Experimental results demonstrate the superiority of the proposed method compared to related works.

Multi-source domain adaptation based tempo-spatial convolution network for cross-subject EEG classification in RSVP task

Objective. Many subject-dependent methods were proposed for electroencephalogram (EEG) classification in rapid serial visual presentation (RSVP) task, which required a large amount of data from new subject and were time-consuming to calibrate system. Cross-subject classification can realize calibration reduction or zero calibration. However, cross-subject classification in RSVP task is still a challenge. Approach. This study proposed a multi-source domain adaptation based tempo-spatial convolution (MDA-TSC) network for cross-subject RSVP classification. The proposed network consisted of three modules. First, the common feature extraction with multi-scale tempo-spatial convolution was constructed to extract domain-invariant features across all subjects, which could improve generalization of the network. Second, the multi-branch domain-specific feature extraction and alignment was conducted to extract and align domain-specific feature distributions of source and target domains in pairs, which could consider feature distribution differences among source domains. Third, the domain-specific classifier was exploited to optimize the network through loss functions and obtain prediction for the target domain. Main results. The proposed network was evaluated on the benchmark RSVP dataset, and the cross-subject classification results showed that the proposed MDA-TSC network outperformed the reference methods. Moreover, the effectiveness of the MDA-TSC network was verified through both ablation studies and visualization. Significance. The proposed network could effectively improve cross-subject classification performance in RSVP task, and was helpful to reduce system calibration time.

Multi-Source Domain Adaptation with Mixture of Joint Distributions

The goal of Multi-Source Domain Adaptation (MSDA) is to train a model (e.g., neural network) with minimal target loss, utilizing training data from multiple source domains (source joint distributions) and a target domain (target joint distribution). The challenge in this problem is that the multiple source joint distributions are different from the target joint distribution. In this paper, we develop a theory that shows a neural network’s target loss is upper bounded by both its source mixture loss (i.e., the loss concerning the source mixture joint distribution) and the Pearson χ2 divergence between the source mixture joint distribution and the target joint distribution. Here, the source mixture joint distribution is the mixture of multiple source joint distributions with mixing weights. Accordingly, we propose an algorithm that optimizes both the mixing weights and the neural network to minimize the estimated source mixture loss and the estimated Pearson χ2 divergence. To estimate the Pearson χ2 divergence, we rewrite it as the maximal value of a quadratic functional, exploit a linear-in-parameter function as the functional’s input, and solve the resultant optimization problem with an analytic solution. This analytic solution allows us to explicitly express the estimated divergence as a loss of the mixing weights and the network’s feature extractor. Finally, we conduct experiments on popular image classification datasets, and the results show that our algorithm statistically outperforms the comparison algorithms. PyTorch code is available at https://github.com/sentaochen/Mixture-of-Joint-Distributions.

A Robust and Real-Time Framework of Cross-Subject Myoelectric Control Model Calibration via Multi-Source Domain Adaptation.

Surface electromyogram (sEMG) has been widely used in hand gesture recognition. However, most previous studies focused on user-personalized models, which require a great amount of data from each new target user to learn the user-specific EMG patterns. In this work, we present a novel real-time gesture recognition framework based on multi-source domain adaptation, which learns extra knowledge from the data of other users, thereby reducing the data collection burdens on the target user. Additionally, compared with conventional domain adaptation methods which treat data from all users in the source domain as a whole, the proposed multi-source method treat data from different users as multiple separate source domains. Therefore, more detailed statistical information on the data distribution from each user can be learned effectively. High-density sEMG (256 channels) from 20 subjects was used to validate the proposed method. Importantly, we evaluated our method with a simulated real-time processing pipeline on continuous sEMG data stream, rather than well-segmented data. The false alarm rate during rest periods in an EMG data stream, which is typically neglected by previous studies performing offline analyses, was also considered. Our results showed that, with only 1 s sEMG data per gesture from the new user, the 10-gesture classification accuracy reached 87.66 % but the false alarm rate was reduced to 1.95 %. Our method can reduce the frustratingly heavy data collection burdens on each new user.

A New Progressive Multisource Domain Adaptation Network With Weighted Decision Fusion.

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.

DANE: A Dual-Level Alignment Network With Ensemble Learning for Multisource Domain Adaptation

DANE: A Dual-Level Alignment Network With Ensemble Learning for Multisource Domain Adaptation

Spectral-Spatial Attention Alignment for Multi-Source Domain Adaptation in EEG-Based Emotion Recognition

Spectral-Spatial Attention Alignment for Multi-Source Domain Adaptation in EEG-Based Emotion Recognition