Multiple Source Domains Research Articles

Generative Inference Network for Imbalanced Domain Generalization.

Domain generalization (DG) aims to learn transferable knowledge from multiple source domains and generalize it to the unseen target domain. To achieve such expectation, the intuitive solution is to seek domain-invariant representations via generative adversarial mechanism or minimization of cross-domain discrepancy. However, the widespread imbalanced data scale problem across source domains and category in real-world applications becomes the key bottleneck of improving generalization ability of model due to its negative effect on learning the robust classification model. Motivated by this observation, we first formulate a practical and challenging imbalance domain generalization (IDG) scenario, and then propose a straightforward but effective novel method generative inference network (GINet), which augments reliable samples for minority domain/category to promote discriminative ability of the learned model. Concretely, GINet utilizes the available cross-domain images from the identical category and estimates their common latent variable, which derives to discover domain-invariant knowledge for unseen target domain. According to these latent variables, our GINet further generates more novel samples with optimal transport constraint and deploys them to enhance the desired model with more robustness and generalization ability. Considerable empirical analysis and ablation studies on three popular benchmarks under normal DG and IDG setups suggests the advantage of our method over other DG methods on elevating model generalization. The source code is available in GitHub https://github.com/HaifengXia/IDG.

A Novel Mix-Normalization Method for Generalizable Multi-Source Person Re-Identification

Person re-identification (Re-ID) has achieved great success in the supervised scenario. However, it is difficult to directly transfer the supervised model to arbitrary unseen domains due to the model overfitting to the seen source domains. In this paper, we aim to tackle the generalizable multi-source person Re-ID task (i.e., there are multiple available source domains, and the testing domain is unseen during training) from the data augmentation perspective, thus we put forward a novel method, termed MixNorm. It consists of domain-aware mix-normalization (DMN) and domain-aware center regularization (DCR). Different from the conventional data augmentation, the proposed domain-aware mix-normalization enhances the diversity of features during training from the normalization perspective of the neural network, which can effectively alleviate the model overfitting to the source domains, so as to boost the generalization capability of the model in the unseen domain. To further promote the efficacy of the proposed DMN, we exploit the domain-aware center regularization to better map the diversely generated features into the same space. Extensive experiments on multiple benchmark datasets validate the effectiveness of the proposed method and show that the proposed method can outperform the state-of-the-art methods. Besides, further analysis also reveals the superiority of the proposed method.

A New Multisource Domain Bearing Fault Diagnosis Method With Adaptive Dual-Domain Obfuscation Weighting Strategy

In the fault diagnosis of modern industrial equipment, domain adaptation is widely utilized to address the challenge of data distribution changes. However, traditional domain adaptation method only learns single domain knowledge and experiences difficulty in coping with scenarios wherein working conditions change drastically. Accordingly, learning knowledge from multiple different source domains can effectively improve the generalization ability of the model and the reliability of the diagnosis results. Simultaneously, aligning marginal and conditional distributions can weaken data distribution differences caused by drastic changes in operating conditions, enabling more accurate knowledge transfer between source and target domains, and improving the performance of model cross-domain fault diagnosis. Furthermore, when the data comes from multiple different domains, completely aligning all source domains is arduous and impractical, and thus, selectively synthesizing different source domain migration diagnostic results is necessary. To address the aforementioned issues, this study proposes a multibranch weight-adaptive adversarial fault diagnosis method. This method uses multiple branch networks to match each source–target domain pair independently, aligns them as much as possible with two domain confusion networks, and calculates the alignment degree to assign the source domain weight coefficient. The final fault diagnosis result is obtained by synthesizing the domain classifier results that correspond to the weighted coefficient domain. Experiments verify the reliability and stability of this method under various working conditions, such as high span speed and variable speed.

Multisource Associate Domain Adaptation for Cross-Subject and Cross-Session EEG Emotion Recognition

Emotion recognition is important in the application of brain-computer interface (BCI). Building a robust emotion recognition model across subjects and sessions is critical in emotion based BCI systems. Electroencephalogram (EEG) is a widely used tool to recognize different emotion states. However, EEG has disadvantages such as small amplitude, low signal-to-noise ratio, and non-stationary properties, resulting in large differences across subjects. To solve these problems, this paper proposes a new emotion recognition method based on a multi-source associate domain adaptation network, considering both domain invariant and domain-specific features. First, separate branches were constructed for multiple source domains, assuming that different EEG data shared the same low-level features. Secondly, the domain specific features were extracted by using the one-to-one associate domain adaptation. Then, the weighted scores of specific sources were obtained according to the distribution distance, and multiple source classifiers were deduced with the corresponding weighted scores. Finally, EEG emotion recognition experiments were conducted on different datasets, including SEED, DEAP, and SEED-IV dataset. Results indicated that, in the cross-subject experiment, the average accuracy in SEED dataset was 86.16%, DEAP dataset was 65.59%, and SEED-IV was 59.29%. In the cross-session experiment, the accuracies of SEED and SEED-IV datasets were 91.10% and 66.68%, respectively. Our proposed method has achieved better classification results compared to state-of-the-art domain adaptation methods.

Latent Feature Disentanglement For Visual Domain Generalization.

Despite remarkable success in a variety of computer vision applications, it is well-known that deep learning can fail catastrophically when presented with out-of-distribution data, where there are usually style differences between the training and test images. Toward addressing this challenge, we consider the domain generalization problem, wherein predictors are trained using data drawn from a family of related training (source) domains and then evaluated on a distinct and unseen test domain. Naively training a model on the aggregate set of data (pooled from all source domains) has been shown to perform suboptimally, since the information learned by that model might be domain-specific and generalizes imperfectly to test domains. Data augmentation has been shown to be an effective approach to overcome this problem. However, its application has been limited to enforcing invariance to simple transformations like rotation, brightness change, etc. Such perturbations do not necessarily cover plausible real-world variations that preserve the semantics of the input (such as a change in the image style). In this paper, taking the advantage of multiple source domains, we propose a novel approach to express and formalize robustness to these kind of real-world image perturbations. The three key ideas underlying our formulation are (1) leveraging disentangled representations of the images to define different factors of variations, (2) generating perturbed images by changing such factors composing the representations of the images, (3) enforcing the learner (classifier) to be invariant to such changes in the images. We use image-to-image translation models to demonstrate the efficacy of this approach. Based on this, we propose a domain-invariant regularization (DIR) loss function that enforces invariant prediction of targets (class labels) across domains which yields improved generalization performance. We demonstrate the effectiveness of our approach on several widely used datasets for the domain generalization problem, on all of which our results are competitive with the state-of-the-art.

Multi-Source Domain Adaptation for Fault Diagnosis of Belt Drives

The implementation effort of data-driven fault diagnosis systems greatly exceeds its economic benefits in many industrial cases. Consequently, highly adapted, individual solutions instead of widespread distribution in the market are currently the result. The biggest problem is the availability of large amounts of labeled data, in particular fault data. In this work, we propose a multi-source domain adaptation procedure that integrates synthetic fault data generation into cross-domain classifier training to overcome this issue. The approach does not require fault data in the target domain which is highly relevant in practice. It is examined using the rarely studied example of diagnosing faulty pretensioning of belt drives. Datasets for multiple domains are collected by attaching different loads to the machine. An extensive experimental study on single and multiple source domains demonstrates the effectiveness of the proposed approach. The generation of fault data outperforms the benchmark methods, especially for multi-source scenarios. Overall, the cross-domain fault diagnosis of belt drives yields promising results to enable a broad range of industrial applications.

MDTL-ACP: Anticancer Peptides Prediction Based on Multi-Domain Transfer Learning.

Anticancer peptides (ACPs) have emerged as one of the most promising therapeutic agents for cancer treatment. They are bioactive peptides featuring broad-spectrum activity and low drug-resistance. The discovery of ACPs via traditional biochemical methods is laborious and costly. Accordingly, various computational methods have been developed to facilitate the discovery of ACPs. However, the data resources and knowledge of ACPs are still very scarce, and only a few of them are clinically verified, which limits the competence of computational methods. To address this issue, in this paper, we propose an ACP prediction model based on multi-domain transfer learning, namely MDTL-ACP, to discriminate novel ACPs from plentiful inactive peptides. In particular, we collect abundant antimicrobial peptides (AMPs) from four well-studied peptide domains and extract their inherent features as the input of MDTL-ACP. The features learned from multiple source domains of AMPs are then transferred into the target prediction task of ACPs via artificial neural network-based shared-extractor and task-specific classifiers in MDTL-ACP. The knowledge captured in the transferred features enhances the prediction of ACPs in the target domain. Experimental results demonstrate that MDTL-ACP can outperform the traditional and state-of-the-art ACP prediction methods. The source code of MDTL-ACP and the data used in this study are available at https://github.com/JunhangCao/MTL-ACP.

Multiple Source Domain Adaptation for Multiple Object Tracking in Satellite Video

Multiple Source Domain Adaptation for Multiple Object Tracking in Satellite Video

A new multi-source Transfer Learning method based on Two-stage Weighted Fusion

Transfer Learning (TL) as an effective tool to solve the classification problem with few or no labeled training data usually confronts with the multiple source domains issue. Fusion of complementary knowledge in different source domains is expected to improve the classification accuracy in the target domain. For this purpose, we present a new Multi-source transfer learning method via Two-stage Weighted Fusion (MTWF). In MTWF, a credal classification rule is first developed to preserve the local imprecise information as much as possible because it could be difficult to commit some patterns to one precise class when using TL techniques. Then, multiple credal classification results characterized by mass function values produced by patterns in each source domain under different feature spaces are locally integrated to extract useful information in each source domain. The locally integrated credal classification results in different source domains are regarded as multiple sources of evidence for global combination using belief functions to make the class decision, and they are first discounted via the weights estimated by domain-consistency. Whereas, there exist some conflicts among these discounted credal classification results, so we proposed to further discount them via Belief Jensen–Shannon (BJS) divergence for the small conflicts. MTWF was compared with a variety of advanced methods, and the experiment results demonstrate that MTWF can significantly improve the classification accuracy.

Class-aware sample reweighting optimal transport for multi-source domain adaptation

Multi-Source Domain Adaptation (MSDA) techniques have attracted widespread attention due to their availability to transfer knowledge from multiple source domains to the unlabeled target domain. Optimal transport (OT) has recently been utilized to measure the distance between distributions in virtue of its robustness. This paper proposes a novel OT-based Class-Aware Sample Reweighting (CASR) method to achieve sample-level fine-grained alignment between multi-source and target. Technically, the class-aware sampling strategy ensures class-level conditional alignment during transport by explicitly selecting samples from each domain. Besides, the sample-reweighting module is designed to allocate specific mass to each transmitted sample, which considers the classification reliability and the spatial information correlation to obtain the alignment priority between target and multi-source and further optimize the transport plan. Extensive experiments conducted on several benchmarks show that CASR presents significant advantages compared with other MSDA methods, and the visualization analysis further demonstrates the effectiveness of each proposed module.

Riemannian representation learning for multi-source domain adaptation

Multi-Source Domain Adaptation (MSDA) aims at training a classification model that achieves small target error, by leveraging labeled data from multiple source domains and unlabeled data from a target domain. The source and target domains are described by related but different joint distributions, which lie on a Riemannian manifold named the statistical manifold. In this paper, we characterize the joint distribution difference by the Hellinger distance, which bears strong connection to the Riemannian metric defined on the statistical manifold. We show that the target error of a neural network classification model is upper bounded by the average source error of the model and the average Hellinger distance, i.e., the average of multiple Hellinger distances between the source and target joint distributions in the network representation space. Motivated by the error bound, we introduce Riemannian Representation Learning (RRL): An approach that trains the network model by minimizing (i) the average empirical Hellinger distance with respect to the representation function, and (ii) the average empirical source error with respect to the network model. Specifically, we derive the average empirical Hellinger distance by constructing and solving unconstrained convex optimization problems whose global optimal solutions are easy to find. With the network model trained, we expect it to achieve small error in the target domain. Our experimental results on several image datasets demonstrate that the proposed RRL approach is statistically better than the comparison methods.

A novel conditional weighting transfer Wasserstein auto-encoder for rolling bearing fault diagnosis with multi-source domains

Transfer learning based on a single source domain to a target domain has received a lot of attention in the cross-domain fault diagnosis tasks of rolling bearing. However, the practical issues often contain multiple source domain data, and the information contained in the target domain is quite different from one source domain to another. Therefore, the transfer pattern from multiple source domains to the target domain undoubtedly has brighter application prospects. Based on these discussions, a multi-source domain transfer learning approach called conditional weighting transfer Wasserstein auto-encoder is developed to deal with the challenges of cross-domain fault diagnosis. Different from the traditional distribution alignment idea of directly aligning the source and target domains, the proposed framework adopts an indirect latent alignment idea to achieve better feature alignment, that is, indirectly aligning the feature distribution of source and target in the latent feature space with the help of Gaussian prior distribution. Furthermore, considering the variability of different source domains containing information about the target domain, an ingenious conditional weighting strategy is designed to quantify the similarity of different source domains to target domain, and further help the proposed model to minimize the discrepancy in conditional distribution. The cross-domain fault diagnosis tasks adequately verify that the proposed framework can sufficiently transfer knowledge from all source domains to the target domain, and has extensive application prospects.

Domain generalization via adversarial out-domain augmentation for remaining useful life prediction of bearings under unseen conditions

Since classical deep learning (DL) techniques are hungry for massive data and suffer from domain shift, domain adaptation (DA) methods are broadly adopted in prognostics and health management (PHM) to align source and target domains. However, DA relies on target datasets collected in advance, which are not always available in practice. In this paper, a domain generalization (DG) approach, which learns from multiple source domains and generalizes well to unseen domains, is introduced for remaining useful life (RUL) prediction of bearings under unseen operating conditions. Specifically, we propose an adversarial out-domain augmentation (AOA) framework to generate pseudo-domains, thereby increasing the diversity of available samples. Hence, a generator is trained in an adversarial manner to generate augmented pseudo-domains by maximizing the domain discrepancy of the latent representations. In addition, we add manifold and semantic regularization to its objective function to ensure the consistency of the pseudo-domains. Trained with these available domains, a task predictor can improve the generalization in inaccessible target domain. Based on this, we provide a specific implementation of AOA-based RUL prediction for DG and validate its effectiveness and superiority using experimental datasets.

Exploring the potential of multi-source unsupervised domain adaptation in crop mapping using Sentinel-2 images

ABSTRACT Accurate crop mapping is critical for agricultural applications. Although studies have combined deep learning methods and time-series satellite images to crop classification with satisfactory results, most of them focused on supervised methods, which are usually applicable to a specific domain and lose their validity in new domains. Unsupervised domain adaptation (UDA) was proposed to solve this limitation by transferring knowledge from source domains with labeled samples to target domains with unlabeled samples. Particularly, multi-source UDA (MUDA) is a powerful extension that leverages knowledge from multiple source domains and can achieve better results in the target domain than single-source UDA (SUDA). However, few studies have explored the potential of MUDA for crop mapping. This study proposed a MUDA crop classification model (MUCCM) for unsupervised crop mapping. Specifically, 11 states in the U.S. were selected as the multi-source domains, and three provinces in Northeast China were selected as individual target domains. Ten spectral bands and five vegetation indexes were collected at a 10-day interval from time-series Sentinel-2 images to build the MUCCM. Subsequently, a SUDA model Domain Adversarial Neural Network (DANN) and two direct transfer methods, namely, the deep neural network and random forest, were constructed and compared with the MUCCM. The results indicated that the UDA models outperformed the direct transfer models significantly, and the MUCCM was superior to the DANN, achieving the highest classification accuracy (OA>85%) in each target domain. In addition, the MUCCM also performed best in in-season forecasting and crop mapping. This study is the first to apply a MUDA to crop classification and demonstrate a novel, effective solution for high-performance crop mapping in regions without labeled samples.

Multi-Source Domain Transfer Discriminative Dictionary Learning Modeling for Electroencephalogram-Based Emotion Recognition

Cognitive computing is dedicated to researching a computing principle and method that can simulate the intelligence ability of human brain. Human emotion is the basic component of human cognitive activities. Electroencephalogram (EEG) computer signals obtained from a brain computer interface are difficult to conceal, and using machine learning methods to analyze EEG emotion is a hot topic in artificial intelligence. However, the EEG signal is non-stationary, making it difficult to select sufficient data from the same person to train a classifier for a subject. To promote the performance of emotion recognition methods, a multi-source domain transfer discriminative dictionary learning modeling (MDTDDL) is proposed in this study. The method integrates transfer learning and dictionary learning in a learning model, including the concepts of subspace learning, manifold smoothness, margin-based discriminant embedding, and large margin. The domain-specific transformation matrix projects EEG signals from various domains into the transfer subspace. The domain-invariant dictionary can find potential connections between multiple source domains and target domain. The manifold smoothness and margin-based discriminant embedding term further improve the model’s learning ability. The alternating optimization technique is used in model solving to efficiently compute model parameters. Experiments on the SEED and DEAP datasets demonstrate the effectiveness of MDTDDL.

Transferable feature filtration network for multi-source domain adaptation

Compared to conventional unsupervised domain adaptation, multi-source domain adaptation (MSDA) is suitable for more practical scenarios but is more challenging because the knowledge transferred to the target domain is learned from multiple source domains. Existing adversarial-based domain adaptation methods facilitate the obfuscation of domain distributions and cross-domain alignment for representations by deceiving the discriminator. However, traditional methods neglect the distinction in the transferability of the different features, resulting in untransferable domain-varying features, such as those extracted from the background, also being forced to align between domains. To this end, we propose a feature filtration mechanism and design a corresponding neural network to achieve a selective feature alignment based on the transferability of features; this is termed a transferable feature filtration network (TFFN). We construct a transferable feature learning framework containing two subprocesses, filtering, in which the attention mechanism-based filtration network automatically extracts transferable features under additional supervision, and repairing, in which we suggest enhancing the adaptability of the filtration network by repairing it with the help of an additional classifier focused on the target knowledge. Furthermore, to facilitate the matching of the source and target distributions at the class level, we propose a filtration consistency loss to enhance the cross-domain consistency of the filtering weights. Extensive experiments conducted on MSDA benchmark datasets show that the proposed method has significant advantages over the existing methods.

Disk anomaly detection for remote maintenance control system of natural gas pipeline based on multi-source domain transfer and incremental learning

Accurate disk anomaly detection is of great significance for maintaining the safe and stable operation of the remote control system of natural gas pipelines. Most disk transfer algorithms use only a single-source domain model for offline transfer learning, resulting in lower classifier performance. This paper proposes a disk online anomaly detection algorithm combining transfer learning and incremental learning. A transfer learning algorithm based on data distribution similarity and dynamic index weighted ensemble is proposed. According to multiple source domain models and target domain model negative data in hyperspace, k neighbour data of each target domain negative data is extracted, and then different test criteria are adopted for each sub-model according to the number of target domain positive data, and index reweighting is performed according to the model detection capability. An incremental learning algorithm based on online automatic annotation is proposed. The data popped up by the sliding window is automatically marked at the current disk state, and the update period of the prediction model is dynamically adjusted according to the false detection rate and the missed detection rate. By setting three different experiments for comparison, the effectiveness and advanced nature of the method proposed in this paper are illustrated.

Multi-Source Contribution Learning for Domain Adaptation.

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.

Multi-source joint domain adaptation for cross-subject and cross-session emotion recognition from electroencephalography.

As an important component to promote the development of affective brain–computer interfaces, the study of emotion recognition based on electroencephalography (EEG) has encountered a difficult challenge; the distribution of EEG data changes among different subjects and at different time periods. Domain adaptation methods can effectively alleviate the generalization problem of EEG emotion recognition models. However, most of them treat multiple source domains, with significantly different distributions, as one single source domain, and only adapt the cross-domain marginal distribution while ignoring the joint distribution difference between the domains. To gain the advantages of multiple source distributions, and better match the distributions of the source and target domains, this paper proposes a novel multi-source joint domain adaptation (MSJDA) network. We first map all domains to a shared feature space and then align the joint distributions of the further extracted private representations and the corresponding classification predictions for each pair of source and target domains. Extensive cross-subject and cross-session experiments on the benchmark dataset, SEED, demonstrate the effectiveness of the proposed model, where more significant classification results are obtained on the more difficult cross-subject emotion recognition task.

Deep Hash with Optimal Transport-Based Domain Adaptation for Multisite MRI Retrieval

The Internet of Things has a wide range of applications in the medical field. Due to the heterogeneity of medical data generated by different hospitals, it is very important to analyze and integrate data from different institutions. Functional magnetic resonance imaging (fMRI) is widely used in clinical medicine and cognitive neuroscience, while resting-state fMRI (rs-fMRI) can help reveal functional biomarkers of neurological disorders for computer-assisted clinical diagnosis and prognosis. Recently, how to retrieve similar images or case histories from large-scale medical image repositories acquired from multiple sites has attracted widespread attention in the field of intelligent diagnosis of diseases. Although using multisite data effectively helps increase the sample size, it also inevitably introduces the problem of data heterogeneity across sites. To address this problem, we propose a multisite fMRI retrieval (MSFR) method that uses a deep hashing approach and an optimal transport-based domain adaptation strategy to mitigate multisite data heterogeneity for accurate fMRI search. Specifically, for a given target domain site and multiple source domain sites, our approach uses a deep neural network to map the source and target domain data into the latent feature space and minimize their Wasserstein distance to reduce their distribution differences. We then use the source domain data to learn high-quality hash code through a global similarity metric, thereby improving the performance of cross-site fMRI retrieval. We evaluated our method on the publicly available Autism Brain Imaging Data Exchange (ABIDE) dataset. Experimental results show the effectiveness of our method in resting-state fMRI retrieval.