Open Set Domain Adaptation Research Articles

Open-set domain adaptation fusion method based on weighted adversarial learning for machinery fault diagnosis

Open-set domain adaptation fusion method based on weighted adversarial learning for machinery fault diagnosis

Few-shot remote sensing image segmentation based on label propagation and open-set domain adaptation

ABSTRACT Few-shot segmentation aims to segment objects in a task dataset with only a few labelled target examples, aided by a lot of labelled auxiliary datasets. Existing approaches typically assume that the task dataset and auxiliary dataset originate from the same source but have disjoint category sets. However, it is common in remote sensing image segmentation tasks that the datasets are from different sources and the category sets are often partially overlapped, where models trained on the auxiliary dataset degrade in performance on the task dataset. To address these issues, we present a few-shot segmentation method based on graph network and open-set domain adaptation. The proposed method utilizes a graph model to capture the relationships between super pixels and to predict labels for the unlabelled samples by label propagation, meanwhile, leverages open-set domain adaptation to reduce inter-domain discrepancies and enhances model transferability. The proposed method is evaluated on two publicly available datasets, and the effectiveness of the proposed approach is demonstrated by the significant improvement in accuracy compared to existing methods.

Open-set domain adaptive fault diagnosis based on supervised contrastive learning and a complementary weighted dual adversarial network

In an actual industrial environment, the complex working environment of mechanical equipment may lead to new faults in the target domain, called the open-set domain adaptation problem. Recently, open-set adaptive fault diagnosis has been extensively employed. However, most studies not only require pre-set fixed thresholds to identify unknown class features but also ignore the learning of discriminable features under specific tasks, which affects the diagnostic performance. Hence, this paper proposes a complementary weighted dual adversarial network combined with supervised contrastive learning (CWDAN-SCL) to address the open-set cross-different working fault diagnosis of bearings. Specifically, a novel complementary weighted adversarial learning strategy is designed using supervised classification and uncertainty measurement to effectively control the participation of target domain features in the domain adaptation process and achieve the alignment of shared class fault features between the source and target domains. Moreover, an adaptive unknown fault separation module is designed using an adversarial learning method to construct a hyperplane between shared and unknown class fault features in the target domain to identify unknown class faults accurately. Additionally, a supervised contrastive loss term is designed based on contrastive learning and label knowledge to improve the aggregation of fault features of the same class and enhance the model’s generalization ability in target domain diagnosis tasks. Subsequently, the efficacy and advancement of the proposed method are substantiated through experimentation on two datasets. The experimental results illustrate that the average diagnostic performance of the proposed method is 91.73 %. This study contributes a dependable diagnostic approach for ascertaining the health status of rotating machinery equipment.

Detecting novel cell type in single-cell chromatin accessibility data via open-set domain adaptation.

Recent advances in single-cell technologies enable the rapid growth of multi-omics data. Cell type annotation is one common task in analyzing single-cell data. It is a challenge that some cell types in the testing set are not present in the training set (i.e. unknown cell types). Most scATAC-seq cell type annotation methods generally assign each cell in the testing set to one known type in the training set but neglect unknown cell types. Here, we present OVAAnno, an automatic cell types annotation method which utilizes open-set domain adaptation to detect unknown cell types in scATAC-seq data. Comprehensive experiments show that OVAAnno successfully identifies known and unknown cell types. Further experiments demonstrate that OVAAnno also performs well on scRNA-seq data. Our codes are available online at https://github.com/lisaber/OVAAnno/tree/master.

Distribution transformer mechanical faults diagnosis method incorporating cross-domain feature extraction and recognition of unknown-type faults

The complexity and uncertainty of vibration signals from distribution transformers pose significant challenges for diagnosing mechanical faults. To address this, this paper proposes a novel fault diagnosis model for distribution transformers, which combines a cross-domain fusion multi-scale convolutional autoencoder (CFMS-CAE) with an open-set domain adaptation classifier (OSDA-C). Specifically, in order to extract more comprehensive features, a convolutional autoencoder (CAE) model based on multi-output objectives is constructed to extract the time-frequency domain characteristics of transformer vibration signals. Multiple-scale convolutional layers are incorporated into the convolutional autoencoder to enable multi-range feature extraction. Additionally, parameter optimization is achieved using the crayfish optimization algorithm (COA). Subsequently, an open-set domain adaptation module is integrated into the convolutional neural network classifier to establish boundaries for each category and facilitate the identification of transformer fault categories, including unknown-type faults. The experimental results demonstrate that the proposed method is effective for fault identification in both dry-type and oil-immersed transformers, with average accuracy reaching 99.35% and 99.62%, respectively. For unknown-type faults, the accuracy also achieved 100% and 97.5%, respectively.

Integrating intrinsic information: A novel open set domain adaptation network for cross-domain fault diagnosis with multiple unknown faults

Existing popular domain adaptation approaches typically assume that the source and target domains share the same label set. However, in industrial scenarios, the equipment may encounter unknown fault modes because of the harsh operating environment, which limits the application of existing diagnostic methods. To address the above problem, an intrinsic information-guided open set domain adaptation network is proposed for cross-domain fault diagnosis with unknown faults. First, a similarity-based discrimination framework is constructed to enhance the robustness of the model for unknown samples, which learns the similarity between samples and fault prototypes to enhance the classification performance. Then, a multi-information integrated weighting module is designed to quantify the transferability of samples through enhanced domain similarity information learning and prediction uncertainty information learning methods. Additionally, a self-supervised neighborhood clustering learning method is constructed, which enables the model to learn structural information about the target domain and encourages the target samples to cluster closely for better separability. Finally, the weighted open set adversarial training framework effectively facilitates diagnostic knowledge transfer and unknown fault recognition. Comprehensive experimental results on two datasets demonstrate the effectiveness of the proposed method in addressing the open set cross-domain diagnosis problem, which achieves promising performance over the comparison methods.

Uncertainty-Aware Aggregation for Federated Open Set Domain Adaptation.

Open set domain adaptation (OSDA) methods have been proposed to leverage the difference between the source and target domains, as well as to recognize the known and unknown classes in the target domain. Such methods typically require the entire source and target data simultaneously to train the target model. However, in real scenarios, data are distributed and stored in various clients. They cannot be exchanged among clients because of privacy protection. Federated learning (FL) is a decentralized approach for training an effective global model with the training data distributed among the clients. Despite its potential in addressing the privacy concerns of data sharing, FL methods for OSDA that can handle unknown classes is not yet available. To tackle this problem, we have developed a novel federated OSDA (FOSDA) algorithm. More specifically, FOSDA adopts an uncertainty-aware mechanism to generate a global model from all client models. It reduces the uncertainty of the federated aggregation by focusing on the contribution of source clients with high uncertainty while retaining those with high consistency. Moreover, a federated class-based weighted strategy is also implemented in FOSDA to maintain the category information of the source clients. We have conducted comprehensive experiments on three benchmark datasets to evaluate the performance of the proposed method, and the results demonstrate the effectiveness of FOSDA.

Open Set Domain Adaptation for Automatic Modulation Classification in Dynamic Communication Environments

Open Set Domain Adaptation for Automatic Modulation Classification in Dynamic Communication Environments

Open-Set Domain Adaptation via Feature Clustering and Separation for Fault Diagnosis

Open-Set Domain Adaptation via Feature Clustering and Separation for Fault Diagnosis

Unknown-class recognition adversarial network for open set domain adaptation fault diagnosis of rotating machinery

Unknown-class recognition adversarial network for open set domain adaptation fault diagnosis of rotating machinery

Enhancing Multi-Source Open-Set Domain Adaptation Through Nearest Neighbor Classification With Self-Supervised Vision Transformer

Enhancing Multi-Source Open-Set Domain Adaptation Through Nearest Neighbor Classification With Self-Supervised Vision Transformer

An auto-regulated universal domain adaptation network for uncertain diagnostic scenarios of rotating machinery

In recent years, domain adaptation techniques have garnered significant attention in the field of intelligent fault diagnosis for mechanical equipment. Domain adaptation techniques can effectively enable the reuse of diagnostic knowledge, thereby improving the generalization performance of the trained model. However, many intelligent diagnostic models are tailored to specific diagnostic scenarios, including closed-set domain adaptation, partial domain adaptation, and open-set domain adaptation. When these models built for specific scenarios are applied to other scenarios, their diagnostic performance will deteriorate catastrophically. This study proposes an auto-regulated universal domain adaptation network (AUDAN) for uncertain diagnostic scenarios of rotating machinery, which can handle multiple diagnostic scenarios and adaptively set threshold boundaries for each fault category based on data distribution structure to distinguish known and unknown samples. Specifically, a novel integration loss function is designed to train the proposed AUDAN. The self-adaptive reweight module is introduced into the proposed method to distribute the relative weight for each loss function automatically. Experiments on two rotating machinery datasets are carried out for validations. The experimental results demonstrate that the proposed AUDAN performs effectively in uncertain diagnostic scenarios and is promising in addressing the fault classification tasks in real industries.

Enhancing open-set domain adaptation through unknown-filtering multi-classifier adversarial network

Domain adaptation is a fundamental research problem that aims to address the domain shift issue during the transfer of a model from a labeled source domain to an unlabeled target domain. In traditional domain adaptation scenarios, it is assumed that the class spaces of both the source and target domains are identical. However, real-world applications often entail situations where the target domain contains private classes that are absent in the source domain. Forcing the alignment of these two domains may result in negative transfer. This specific concern is addressed by the emerging field of Open-set Domain Adaptation (OSDA). Previous OSDA methods attempted to align the known classes between the source and target domains while separating the unknown samples. However, these methods are inadequate in effectively discerning instances from the unknown classes. Therefore, we propose Enhancing Open-Set Domain Adaptation through Unknown-Filtering Multi-Classifier Adversarial Network (UFMCAN), which leverages multiple classifiers including a weighted auxiliary classifier, an open-set recognizer, a primary classifier and a three-way domain discriminator through adversarial learning to effectively filter instances from the unknown classes present in the target domain while concurrently aligning the source and target-known distribution. Experiments on extensive benchmarks (Office-31, Office-Home, VisDA-2017 and Office-Mix) demonstrate the superior performance of UFMCAN compared to existing state-of-the-art methods.

Open-set adversarial domain match for electronic nose drift compensation and unknown gas recognition

In practical applications, electronic noses (EN) must tackle not only sensor drift but also resist interference from unknown gases. In prolonged open environments, electronic noses often encounter unknown gases that cannot be predicted in advance. Current gas detection methods typically rely on prior knowledge of target analytes but struggle to comprehensively address sensor drift and interference from unknown gases. Such as indoor air quality monitoring, long-term stability is crucial, and not all relevant analytes can be predetermined. However, the recent research cannot solve both the sensor drift and unknown gas intrusion problem simultaneously well. In this work, we unify above problems in to the open-set risk boundary. We propose an open-set adversarial domain match (OSADM) model and introduce the considers of open-set domain adaptation (OSDA). OSDA trains a target classifier through matching the domain distribution to recognize the known and unknown gases. First, a binary adversarial loss divides the class boundary. Secondly, adversarial domain adaptation unifies the distribution of different domains. Compared with the metric methods, it avoids complex distribution computation and parameter adjustment to reduce negative transfer. Extensive experimental results on two benchmark datasets, Gas Sensor Array Drift and Twin gas sensor arrays Dataset show that OSADM outperformance of the existing open-set models.

Cluster-based Adversarial Decision Boundary for domain-adaptive open set recognition

Domain adaptation has achieved significant progress recently by adapting models trained on a source domain to an unlabeled target domain. Open Set Domain adaptation (OSDA) has drawn much attention nowadays, where the target domain contains some exclusive categories other than the source domain’s known classes. With no label in the target data, existing OSDA methods often suffer from negative transfer. Conventional methods for unknown class rejection require an empirical setting of the confidence threshold, which lacks flexibility since the model confidence may vary during the training process, and our motivation is to omit the effort of setting the rejection threshold manually. Based on the idea that latent features of the same class should be in the same cluster to address this issue, we propose a domain adaptive open set recognition framework: Cluster-based Adversarial Decision Boundary (CADB). Specifically, we design an end-to-end unknown class rejection model consisting of three components: known class prototype estimation under the cluster assumption; known class similarity score estimation; and adaptive unknown class rejection threshold generation with adversarial feature suppression. These three components work as one entity to give a similarity score for each sample. Those samples that are less similar to the cluster prototype compared with the counterfactual features are rejected as the unknown class. Extensive evaluations are conducted to verify the effectiveness and robustness of the proposed boundary generation procedure.

Open Set Domain Adaptation for Image Classification With Multiple Unknown Labels Using Unsupervised Clustering in a Target Domain

Open Set Domain Adaptation for Image Classification With Multiple Unknown Labels Using Unsupervised Clustering in a Target Domain

Subdomain alignment based open-set domain adaptation image classification

Domain adaptation has achieved great success in using labeled source domain samples to identify unlabeled target domain samples. Here, we aim to solve the open-set domain adaptation, which is different from the closed-set domain adaptation in that it contains categories in target domain that do not appear in source domain. To solve this problem, this paper proposes open-set domain adaptation model based on subdomain alignment, which uses variable weights for discriminative training of unknown samples in target domain. Aiming at the distribution differences between domains, the model aligns the distributions of the category subspaces of source and target domains, enhancing the distribution similarity within the subspaces of the same category. Through experiments on different domain adaptation datasets, the results show that the model proposed in this paper effectively improves the accuracy of open-set domain adaptation classification.

Self-Labeling Framework for Open-Set Domain Adaptation With Few Labeled Samples

Unsupervised domain adaptation (UDA) is extremely effective for transferring knowledge from a label-rich source domain to a label-scarce target domain. Because the target domain is unlabeled and may contain additional novel classes, open-set domain adaptation (ODA) has been suggested as a possible solution to detect these novel classes in the training phase. However, existing ODA methods rely heavily on abundant fully labeled source data, which are expensive to collect in specific applications and may also contain novel classes. In this study, we propose a novel self-labeling framework with prototypical contrastive learning and mutual information maximization to achieve ODA even when the amount of labeled data is very small, which is a new problem setting named few-shot ODA (FODA). We use self-supervised prototypical contrastive learning to train the network to learn the representations of source and target samples and maximize the mutual information between labels and input data to simultaneously recognize known and novel classes in the source and target domains. We evaluated our strategy in several domain adaptation environments and found that our method performed far better than existing approaches.

Dynamically Self-Training Open Set Domain Adaptation Classification Method for Heterogeneous SAR Image

Dynamically Self-Training Open Set Domain Adaptation Classification Method for Heterogeneous SAR Image

Imbalanced Open Set Domain Adaptation via Moving-Threshold Estimation and Gradual Alignment

Imbalanced Open Set Domain Adaptation via Moving-Threshold Estimation and Gradual Alignment