Labeled Data In Source Domain Research Articles

CSF: Closed-mask-guided semantic fusion method for semantic perception of unknown scenes

• A mask-guided semantic fusion method for optimizing the results of semantic segmentation. • A closed mask generation (CEG) module for post-processing of the edge detection algorithm. • A semantic confidence fusion (SCF) module to fuse the semantic segmentation results. • This method does not need to obtain the target data in advance. • This method has been verified in two scenarios. Though many high-precision semantic segmentation models have been proposed, how to improve the generalization ability of these models is still an urgent problem. Recently, a great number of unsupervised domain adaptation (UDA) algorithms around domain adaptation problems have been studied in semantic segmentation. These methods require labeled source domain data and unlabeled target domain data. In this paper, we propose a closed-mask-guided semantic fusion method (CSF) to improve the semantic segmentation results of unknown scenes, where the target domain data is not obtained in advance. First, a Closed Mask Generation (CMG) module is designed to convert the edge detection result into a mask that can segment the image into several image blocks. Then, a Semantic Confidence Fusion (SCF) module based on information entropy and voting method is introduced, which can select reliable semantic segmentation results for each image block by comparing the confidence of several semantic segmentation networks . In addition, the experimental results on both KITTI and COCO Stuff datasets validate the effectiveness of this method. The code is publicly available at https://github.com/tryhere/CSF .

Domain‐specific feature recalibration and alignment for multi‐source unsupervised domain adaptation

Traditional unsupervised domain adaptation (UDA) usually assumes that the source domain has labels and the target domain has no labels. In a real environment, labelled source domain data usually comes from multiple different distributions. To handle this problem, multi-source unsupervised domain adaptation (MUDA) is proposed. Multi-source unsupervised domain adaptation aims to adapt the model trained on multi-labelled source domains to the unlabelled target domain. In this paper, a novel MUDA method by domain-specific feature recalibration and alignment (FRA) is proposed. Specifically, to achieve feature recalibration, the authors leverage channel attention to pick out significant channels and spatial attention to focus on important features in different channels. Such integration of channel and spatial attention can lead to effective domain-specific feature recalibration that may be of great importance to MUDA. In addition, to achieve better MUDA, the authors propose domain-specific feature alignment which consists of Maximum Mean Discrepancy and JS-divergence loss. Maximum Mean Discrepancy can reduce the difference between the source domain and target domain. Meanwhile, JS-divergence loss may ensure the prediction consistency of different classifiers in the source domains. Four experiments have proved that FRA can achieve significantly better results in popular benchmarks for MUDA.

Unsupervised domain adaptation in homogeneous distance space for person re-identification

Data distribution alignment and clustering-based self-training are two feasible solutions to tackle unsupervised domain adaptation (UDA) on person re-identification (re-ID). Most existing alignment-based methods solely learn the source domain decision boundaries and align the data distribution of the target domain to the source domain, thus the re-ID performance on the target domain completely depends on the shared decision boundaries and how well the alignment is performed. However, two domains can hardly be precisely aligned because of the label space discrepancy of two domains, resulting in poor target domain re-ID performance. Although clustering-based self-training approaches could learn independent decision boundaries on the pseudo-labelled target domain data, they ignore both the accurate ID-related information of the labelled source domain data and the underlying relations between two domains. To fully exploit the source domain data to learn discriminative target domain ID-related features, in this paper, we propose a novel cross-domain alignment method in the homogeneous distance space, which is constructed by the newly designed stair-stepping alignment (SSA) matcher. Such alignment method can be integrated into both alignment-based framework and clustering-based framework. Extensive experiments validate the effectiveness of our proposed alignment method in these two frameworks. We achieve superior performance when the proposed alignment module is integrated into the clustering-based framework. Codes will be available at: http://github.com/Dingyuan-Zheng/HDS.

Self-rule to multi-adapt: Generalized multi-source feature learning using unsupervised domain adaptation for colorectal cancer tissue detection.

Supervised learning is constrained by the availability of labeled data, which are especially expensive to acquire in the field of digital pathology. Making use of open-source data for pre-training or using domain adaptation can be a way to overcome this issue. However, pre-trained networks often fail to generalize to new test domains that are not distributed identically due to tissue stainings, types, and textures variations. Additionally, current domain adaptation methods mainly rely on fully-labeled source datasets. In this work, we propose Self-Rule to Multi-Adapt (SRMA), which takes advantage of self-supervised learning to perform domain adaptation, and removes the necessity of fully-labeled source datasets. SRMA can effectively transfer the discriminative knowledge obtained from a few labeled source domain's data to a new target domain without requiring additional tissue annotations. Our method harnesses both domains' structures by capturing visual similarity with intra-domain and cross-domain self-supervision. Moreover, we present a generalized formulation of our approach that allows the framework to learn from multiple source domains. We show that our proposed method outperforms baselines for domain adaptation of colorectal tissue type classification in single and multi-source settings, and further validate our approach on an in-house clinical cohort. The code and trained models are available open-source: https://github.com/christianabbet/SRA.

Pinball transfer support matrix machine for roller bearing fault diagnosis under limited annotation data

As a classical matrix classification technology, support matrix machine (SMM) takes the matrix as the input element, so that the structure information between matrix samples is maximized to establish an accurate classification model. However, in practical industrial practice, it is difficult to collect enough annotation samples for SMM to find the optimal hyperplane. To solve this issue, a new pinball transfer support matrix machine (PTSMM) method is proposed in this paper. In the case of limited annotation sample, PTSMM trains a model using a large amount of labeled source domain data, and then fine-tunes the prior model using a small amount of target domain data, so as to obtain an optimal model for target domain. Besides, the pinball attribute is applied to the objective function to make the direct distance of the hyperplane larger, which weakens the influence of noise on the hyperplane. The two different roller bearing experiment results show that PTSMM effectively uses the samples of source domain and target domain for modeling, and the diagnostic accuracy is more than 4% higher than SMM and its improved algorithms.

Unsupervised domain adaptation via distilled discriminative clustering

Unsupervised domain adaptation addresses the problem of classifying data in an unlabeled target domain, given labeled source domain data that share a common label space but follow a different distribution. Most of the recent methods take the approach of explicitly aligning feature distributions between the two domains. Differently, motivated by the fundamental assumption for domain adaptability, we re-cast the domain adaptation problem as discriminative clustering of target data, given strong privileged information provided by the closely related, labeled source data. Technically, we use clustering objectives based on a robust variant of entropy minimization that adaptively filters target data, a soft Fisher-like criterion, and additionally the cluster ordering via centroid classification. To distill discriminative source information for target clustering, we propose to jointly train the network using parallel, supervised learning objectives over labeled source data. We term our method of distilled discriminative clustering for domain adaptation as DisClusterDA. We also give geometric intuition that illustrates how constituent objectives of DisClusterDA help learn class-wisely pure, compact feature distributions. We conduct careful ablation studies and extensive experiments on five popular benchmark datasets, including a multi-source domain adaptation one. Based on commonly used backbone networks, DisClusterDA outperforms existing methods on these benchmarks. It is also interesting to observe that in our DisClusterDA framework, adding an additional loss term that explicitly learns to align class-level feature distributions across domains does harm to the adaptation performance, though more careful studies in different algorithmic frameworks are to be conducted.

Federated Transfer Learning for Bearing Fault Diagnosis With Discrepancy-Based Weighted Federated Averaging

Generally, high performance of deep learning (DL)-based machinery fault diagnosis methods relies on abundant labeled fault samples under various working conditions, while they are usually stored by different users and not communicated with each other due to data privacy protection. Federated learning (FL) is a possible solution, but the traditional federated averaging (FedAvg) algorithm in FL ignores the potential domain shift of different FL participants, which limits its further application. Therefore, a federated transfer learning framework with discrepancy-based weighted federated averaging (D-WFA) is proposed to train the good global diagnosis model collaboratively as well as protect data privacy. First, local labeled source domain data and unlabeled target domain data are utilized to update multiple local models with generalization ability. Then, a maximum mean discrepancy (MMD)-based dynamic weighted averaging algorithm is designed to aggregate the updated local models with automatically learned weight. The proposed D-WFA overcomes the disadvantage of the traditional FedAvg algorithm which assumes all clients have the same contribution in constructing the global model during FL training. Experiment results on a bearing dataset show that the proposed D-WFA outperforms the traditional FedAvg and relative federated transfer learning method, which offers a promising solution in privacy-preserving machine learning for fault diagnosis.

Easy Domain Adaptation for cross-subject multi-view emotion recognition

Existing domain adaptation methods for cross-subject emotion recognition are primarily focused on accuracy and suffer from the issues of intensive hyperparameter tunings and high computational complexity. In this paper, we make the first attempt to address these issues by developing a domain-invariant classifier called Easy Domain Adaptation (EasyDA) based on multi-view emotion inputs (multiple modalities or multiple types of features). Firstly, EasyDA uses both the source domain (training subjects) and the target domain (test subject) to generate domain generalization features for each view by leveraging a fast, accurate, and low-memory approximate empirical kernel map (AEKM), followed by a parameterless weighted combination for multi-views. Secondly, EasyDA simultaneously learns an optimal separating hyperplane and the pseudo labels for the target domain such that (a) high classification accuracies are obtained on both labeled source domain data and the pseudo-labeled target domain data; (b) the distribution distance between source and target domain is reduced; (c) the predicted output vector in the target domain changes little over time in short time intervals, based on the biological evidence that emotion varies fluently and smoothly. Eventually, by summarizing these two steps with the ridge regression theory and alternating optimization, EasyDA can transfer knowledge across domains accurately, efficiently, and easily in a unified framework. Experimental results on the SEED and SEED-IV datasets demonstrate that EasyDA significantly outperforms multiple representative domain adaptation methods in terms of accuracy, computational time, and memory consumption. It is noteworthy that EasyDA achieves satisfactory performance under a wide range of parameter settings.

A novel multi-adversarial cross-domain neural network for bearing fault diagnosis

Recently, deep neural networks have achieved great success in bearing fault diagnosis. Most existing methods are developed under the assumption that the bearing vibration signals are collected under the same machine operating conditions. However, bearing fault diagnosis under cross-domain conditions will suffer from domain shift problems if the neural network is only trained with the source domain data. Moreover, acquiring enough labeled data from the target domain will be expensive and time-consuming. To address the above problems, this paper proposes an end-to-end multi-adversarial cross-domain neural network for bearing fault diagnosis, which takes labeled source domain data and unlabeled target domain data to achieve the cross-domain bearing fault diagnosis under cross-load conditions and cross-machine conditions. The proposed method employs multi-adversarial training to automatically extract the domain-invariant features from source and target domains instead of manually designing features, which combines domain-adversarial learning and mini-max entropy adversarial learning to adversarially reduce the domain discrepancy between the source and target domains and alleviate the class misalignment problem. The results of the cross-load and the cross-machine experiments prove the effectiveness of the proposed method, and the proposed method provides a promising tool for cross-domain bearing fault diagnosis.

Design and Implementation of Human-Computer Interaction Systems Based on Transfer Support Vector Machine and EEG Signal for Depression Patients’ Emotion Recognition

At present, the demand for intelligentization of human-computer interaction systems (HCIS) has become increasingly prominent. Being able to recognize the emotions of users of interactive systems is a distinguishing feature of intelligent interactive systems. The intelligent HCIS can analyze the emotional changes of patients with depression, complete the interaction with the patients in a more appropriate manner, and the recognition results can assist family members or medical personnel to make response measures based on the patient’s emotional changes. Based on this background, this paper proposes a sentiment recognition method based on transfer support vector machines (TSVM) and EEG signals. The ER (ER) results based on this method are applied to HCIS. Such a HCIS is mainly used for the interaction of patients with depression. When a new field related to a certain field appears, if the new field data is relabeled, the sample is expensive, and it is very wasteful to discard all the old field data. The main innovation of this research is that the introduced classification model is TSVM. TSVM is a transfer learning strategy based on SVM. Transfer learning aims to solve related but different target domain problems by using a large amount of labeled source domain data. Therefore, the transfer support vector machine based on the transfer mechanism can use the small labeled data of the target domain and a large amount of old data in the related domain to build a high-quality classification model for the target domain, which can effectively improve the accuracy of classification. Comparing the classification results with other classification models, it can be concluded that TSVM can effectively improve the accuracy of ER in patients with depression. The HCIS based on the classification model has higher accuracy and better stability.

Graph Domain Adversarial Transfer Network for Cross-Domain Sentiment Classification

In the text sentiment classification task, some words are seemingly unrelated to the classification task, but they have a direct impact on the performance of classification model. For example, in the sentences “I have terminal cancer” and “Cancer is a very common disease”, it can be clearly found that the word “cancer” has two different sentiment tendencies in the daily life domain and the medical domain. In the daily life domain, the word “cancer” shows an extremely negative sentiment tendency. While in the medical domain, the word “cancer” is just a simple term with a relatively neutral sentiment tendency. Although current deep learning models have already achieved good performance through their powerful feature learning capabilities, there are serious deficiencies in dealing with the above problem. Therefore, from a new perspective, this paper proposes the Graph Domain Adversarial Transfer Network (GDATN) based on the idea of adversarial learning, which uses the labeled source domain data to predict the sentiment label of unlabeled target domain data. Firstly, GDATN extracts feature representations through the Bidirectional Long Short-Term Memory (BiLSTM) Network and Graph Attention Network (GAT) successively. Then, GDATN introduces the domain classifier to capture the domain-shared text feature representation with the Gradient Reversal Layer (GRL). In addition, an auxiliary task named the projection mechanism is constructed to further capture the domain-specific text feature representation in response to the text domain problem. Extensive experimental results on two benchmark datasets show that GDATN proposed in this paper outperforms the other six benchmark sentiment classification models, and GDATN has a better stability on different cross-domain pairs.

Compact class-conditional domain invariant learning for multi-class domain adaptation

Neural network-based models have recently shown excellent performance in various kinds of tasks. However, a large amount of labeled data is required to train deep networks, and the cost of gathering labeled training data for every kind of domain is prohibitively expensive. Domain adaptation tries to solve this problem by transferring knowledge from labeled source domain data to unlabeled target domain data. Previous research tried to learn domain-invariant features of source and target domains to address this problem, and this approach has been used as a key concept in various methods. However, domain-invariant features do not mean that a classifier trained on source data can be directly applied to target data because it does not guarantee that data distribution of the same classes will be aligned across two domains. In this paper, we present novel generalization upper bounds for domain adaptation that motivates the need for class-conditional domain invariant learning. Based on this theoretical framework, we then propose a class-conditional domain invariant learning method that can learn a feature space in which features in the same class are expected to be mapped nearby. We empirically experimented that our model showed state-of-the-art performance on standard datasets and showed effectiveness by visualization of latent space.

A New Deep Convolutional Domain Adaptation Network for Bearing Fault Diagnosis under Different Working Conditions

Effective fault diagnosis methods can ensure the safe and reliable operation of the machines. In recent years, deep learning technology has been applied to diagnose various mechanical equipment faults. However, in real industries, the data distribution under different working conditions is often different, which leads to serious degradation of diagnostic performance. In order to solve the issue, this study proposes a new deep convolutional domain adaptation network (DCDAN) method for bearing fault diagnosis. This method implements cross-domain fault diagnosis by using the labeled source domain data and the unlabeled target domain data as training data. In DCDAN, firstly, a convolutional neural network is applied to extract features of source domain data and target domain data. Then, the domain distribution discrepancy is reduced through minimizing probability distribution distance of multiple kernel maximum mean discrepancies (MK-MMD) and maximizing the domain recognition error of domain classifier. Finally, the source domain classification error is minimized. Extensive experiments on two rolling bearing datasets verify that the proposed method can implement accurate cross-domain fault diagnosis under different working conditions. The study may provide a promising tool for bearing fault diagnosis under different working conditions.

Enhancing unsupervised domain adaptation by discriminative relevance regularization

Unsupervised domain adaptation (UDA) serves to transfer specific knowledge from massive labeled source domain data to unlabeled target domain data via mitigating domain shift. In this paper, we propose a discriminative relevance regularization term (DRR) to enhance the performance of UDA by reducing the domain shift from the aspect of semantic relevance across domains. In particular, DRR is formulated as the min–max rank problem which seeks a projection matrix to minimize the rank of intra-class projected features and maximize the rank of the means of inter-class projected features simultaneously. To test the potential effectiveness of DRR, we design a relevance regularized distribution adaptation method (RRDA) and relevance regularized adaptation networks (RRAN) for image classification, and a relevance regularized self-supervised learning method (RRSL) for semantic segmentation by incorporation of DRR. The corresponding optimization algorithms are proposed to solve them. Experiments of cross-domain image classification show that both RRDA and RRAN outperform several state-of-the-art compared methods. Moreover, experiments of domain-adaptation semantic segmentation on two synthetic-to-real segmentation datasets demonstrate the capacity of RRSL. Such results imply the efficacy of DRR on both image classification and semantic segmentation tasks.

Transfer Change Rules from Recurrent Fully Convolutional Networks for Hyperspectral Unmanned Aerial Vehicle Images without Ground Truth Data

Change detection (CD) networks based on supervised learning have been used in diverse CD tasks. However, such supervised CD networks require a large amount of data and only use information from current images. In addition, it is time consuming to manually acquire the ground truth data for newly obtained images. Here, we proposed a novel method for CD in case of a lack of training data in an area near by another one with the available ground truth data. The proposed method automatically entails generating training data and fine-tuning the CD network. To detect changes in target images without ground truth data, the difference images were generated using spectral similarity measure, and the training data were selected via fuzzy c-means clustering. Recurrent fully convolutional networks with multiscale three-dimensional filters were used to extract objects of various sizes from unmanned aerial vehicle (UAV) images. The CD network was pre-trained on labeled source domain data; then, the network was fine-tuned on target images using generated training data. Two further CD networks were trained with a combined weighted loss function. The training data in the target domain were iteratively updated using he prediction map of the CD network. Experiments on two hyperspectral UAV datasets confirmed that the proposed method is capable of transferring change rules and improving CD results based on training data extracted in an unsupervised way.

Deep learning-based cross-domain adaptation for gearbox fault diagnosis under variable speed conditions

Existing intelligent gearbox fault diagnosis approaches have two shortcomings: (a) their performance is mostly confined to manual handcrafted features, and (b) they follow a general assumption that the distribution of the data in the source domain (labeled data on which the model is trained) is similar to the target domain (unlabeled data on which the model is tested), which might not be the case in real-world applications. Substantial human expertise and domain knowledge is required for manual feature extraction, and moreover, deploying the same model for a target domain whose distribution is different from the source domain would lead to poor generalization. Since deep learning methods can automatically learn high dimensional feature representations from raw measurement data, this paper proposes a novel deep learning-based domain adaptation (DA) method for gearbox fault diagnosis under significant speed variations. A deep convolutional neural network is used as the main architecture. The paper proposes to minimize the summation of cross-entropy loss (between the labeled source domain data) and maximum mean discrepancy loss (between the labeled source and unlabeled target datasets) simultaneously to adapt the source domain model to be applied in the target domain. The proposed deep learning DA approach is evaluated using experimental data from a gearbox under variable speeds and multiple health conditions. An appropriate benchmarking with both traditional machine learning methods and other DA methods demonstrate the superiority of the proposed method.

Deep joint two-stream Wasserstein auto-encoder and selective attention alignment for unsupervised domain adaptation

Domain adaptation refers to the process of utilizing the labeled source domain data to learn a model that can perform well in the target domain with limited or missing labels. Several domain adaptation methods combining image translation and feature alignment have been recently proposed. However, there are two primary drawbacks of such methods. First, the majority of the methods assume that synthetic target images have the same distribution as real target images, and thus, only the synthetic target images are employed for training the target classifier, which makes the model’s performance significantly dependent on the quality of the generated images. Second, most of the methods blindly align the discriminative content information by merging spatial and channel-wise information, thereby ignoring the relationships among channels. To address these issues, a two-step approach that joints two-stream Wasserstein auto-encoder (WAE) and selective attention (SA) alignment, named J2WSA, is proposed in this study. In the pre-training step, the two-stream WAE is employed for mapping the four domains to a shared nice manifold structure by minimizing the Wasserstein distance between the distribution of each domain and the corresponding prior distribution. During the fine-tuning step, the SA alignment model initialized by the two-stream WAE is applied for automatically selecting the style part of channels for alignment, while simultaneously suppressing the content part alignment using the SA block. Extensive experiments indicate that the combination of the aforementioned two models can achieve state-of-the-art performance on the Office-31 and digital domain adaptation benchmarks.

Multitask Learning for Cross-Domain Image Captioning

Recent artificial intelligence research has witnessed great interest in automatically generating text descriptions of images, which are known as the image captioning task. Remarkable success has been achieved on domains where a large number of paired data in multimedia are available. Nevertheless, annotating sufficient data is labor-intensive and time-consuming, establishing significant barriers for adapting the image captioning systems to new domains. In this study, we introduc a novel Multitask Learning Algorithm for cross-Domain Image Captioning (MLADIC). MLADIC is a multitask system that simultaneously optimizes two coupled objectives via a dual learning mechanism: image captioning and text-to-image synthesis, with the hope that by leveraging the correlation of the two dual tasks, we are able to enhance the image captioning performance in the target domain. Concretely, the image captioning task is trained with an encoder–decoder model (i.e., CNN-LSTM) to generate textual descriptions of the input images. The image synthesis task employs the conditional generative adversarial network (C-GAN) to synthesize plausible images based on text descriptions. In C-GAN, a generative model $G$ synthesizes plausible images given text descriptions, and a discriminative model $D$ tries to distinguish the images in training data from the generated images by $G$ . The adversarial process can eventually guide $G$ to generate plausible and high-quality images. To bridge the gap between different domains, a two-step strategy is adopted in order to transfer knowledge from the source domains to the target domains. First, we pre-train the model to learn the alignment between the neural representations of images and that of text data with the sufficient labeled source domain data. Second, we fine-tune the learned model by leveraging the limited image–text pairs and unpaired data in the target domain. We conduct extensive experiments to evaluate the performance of MLADIC by using the MSCOCO as the source domain data, and using Flickr30k and Oxford-102 as the target domain data. The results demonstrate that MLADIC achieves substantially better performance than the strong competitors for the cross-domain image captioning task.

A framework for semi-supervised metric transfer learning on manifolds

A common assumption of statistical learning theory is that the training and testing data are drawn from the same distribution. However, in many real-world applications, this assumption does not hold true. Hence, a realistic strategy, Cross Domain Adaptation (DA) or Transfer Learning (TA), can be used to employ previously labelled source domain data to boost the task in the new target domain. Previously, Cross Domain Adaptation methods have been focused on re-weighting the instances or aligning the cross-domain distributions. However, these methods have two significant challenges: (1) There is no proper consideration of the unlabelled data of target task as in the real-world, an abundant amount of unlabelled data is available, (2) The use of normal Euclidean distance function fails to capture the appropriate similarity or dissimilarity between samples. To deal with this issue, we have proposed a Semi-Supervised Metric Transfer Learning framework called SSMT that reduces the distribution between domains both statistically and geometrically by learning the instance weights, while a regularized distance metric is learned to minimize the within-class co-variance and maximize the between-class co-variance simultaneously for the target domain. Compared with the previous works where Mahalanobis distance metric and instance weights are learned by using the labelled data or in a pipelined framework that leads to a decrease in the performance, our proposed SSMT attempts to learn a regularized distance metric and instance weights by considering unlabelled data in a parallel framework. Experimental evaluation on three cross-domain visual data sets, e.g., PIE Face, Handwriting Digit Recognition on MNIST–USPS and Object Recognition, demonstrates the effectiveness of our designed approach on facilitating the unlabelled target task learning, compared to current state-of-the-art domain adaptation approaches.

Transfer Learning with Joint Distribution Adaptation and Maximum Margin Criterion

Transfer learning aims to transfer knowledge from the labelled source domain data to build a good classifier for the target domain data which has few labels or none. Existing feature-based transfer learning methods seek to transform the data to a new feature space under which the distribution discrepancy is reduced. However, data in different classes may not be easy to be separated in the new feature representation. Therefore, a modified transfer learning algorithm with Joint Distribution Adaptation (JDA) and Maximum Margin Criterion (MMC) is put forward in this paper, which we call MMC-JDA for short. MMC-JDA is aimed at minimizing the distribution discrepancy between two domains and maximizing the separability of each class at the same time. Comparative experiments on 16 cross-domain public image datasets show that MMC-JDA is effective and performs better than several common transfer learning methods.