Unlabeled Data In Target Domain Research Articles

A Novel Fault Diagnosis Method for Rolling Bearings Based on Multi-Domain Adaptation Neural Network

In recent years, data-driven approaches have shown promise in fault diagnosis. However, in practical industrial applications, challenges such as discrepancies between source domain and target domain data distributions, coupled with limited labeled fault data, have hindered the performance of traditional domain adaptation algorithms in bearing fault diagnosis. To address this issue, this study introduces a novel method based on MDANN (Multi Domain Adaptation Neural Network) for diagnosing rolling bearing faults using unlabeled data. Initially, the raw vibration signals are preprocessed using wavelet packet decomposition and reconstruction (WPT), which helps minimize signal redundancy while preserving critical features. Following this, the multi-kernel maximum mean discrepancy (MK-MMD) algorithm is employed to compute the differences in input feature values, and the MDANN network parameters are adjusted via backpropagation, allowing the network to extract domain-invariant features. To ensure the unlabeled target domain data can be effectively used in training, a pseudo-labeling strategy is applied, where the label with the highest probability is treated as the true label, thereby enabling the model to learn from the target domain data and improve the acquisition of reliable diagnostic knowledge. The method is validated using two publicly available datasets, CWRU and PU, with experimental results demonstrating that it outperforms conventional domain adaptation techniques in terms of diagnostic accuracy. This indicates the proposed approach is highly effective in capturing transferable features and addressing the distributional differences between datasets.

A Collaborative Domain Adversarial Network for Unlabeled Bearing Fault Diagnosis

At present, data-driven fault diagnosis has made significant achievements. However, in actual industrial environments, labeled fault data are difficult to obtain, making the industrial application of intelligent fault diagnosis models very challenging. This limitation even prevents intelligent fault diagnosis algorithms from being applicable in real-world industrial settings. In light of this, this paper proposes a Collaborative Domain Adversarial Network (CDAN) method for the fault diagnosis of rolling bearings using unlabeled data. First, two types of feature extractors are employed to extract features from both the source and target domain samples, reducing signal redundancy and avoiding the loss of critical signal features. Second, the multi-kernel clustering algorithm is used to compute the differences in input feature values, create pseudo-labels for the target domain samples, and update the CDAN network parameters through backpropagation, enabling the network to extract domain-invariant features. Finally, to ensure that unlabeled target domain data can participate in network training, a pseudo-label strategy using the maximum probability label as the true label is employed, addressing the issue of unlabeled target domain data not being trainable and enhancing the model’s ability to acquire reliable diagnostic knowledge. This paper validates the CDAN using two publicly available datasets, CWRU and PU. Compared with four other advanced methods, the CDAN method improved the average recognition accuracy by 7.85% and 5.22%, respectively. This indirectly proves the effectiveness and superiority of the CDAN in identifying unlabeled bearing faults.

Anti-forgetting source-free domain adaptation method for machine fault diagnosis

Unsupervised domain adaptation methods have made significant progress in the field of machine fault diagnosis. However, these methods generally assume the accessibility of source domain data during the cross-domain transfer phase. In practice, this assumption is often impractical due to data privacy requirements and the burden of data transmission and storage. Additionally, a key challenge lies in preventing the forgetting of source domain knowledge while performing cross-domain diagnosis in a source-free scenario. To address these issues, this paper proposes an anti-forgetting source-free domain adaptation method for machine fault diagnosis. Specifically, a prototype-based pseudo-label generation strategy, combined with a nearest-neighbor constraint, is employed to deeply explore diagnosis information in the unlabeled target domain data. Then, a fast nuclear-norm maximization constraint is introduced to reduce the density of target domain samples near the classification boundary, thereby decreasing the probability of misdiagnosis. Furthermore, a weighted Fisher regularization is designed to retain the diagnosis information of healthy states inherent in the source domain, thus mitigating information forgetting. Finally, comprehensive experiments are conducted to validate the superiority of our method in source-free cross-domain diagnosis from multiple perspectives, while also demonstrating its anti-forgetting properties.

Adaptive wavelet-VNet for single-sample test time adaptation in medical image segmentation.

In medical image segmentation, a domain gap often exists between training and testing datasets due to different scanners or imaging protocols, which leads to performance degradation in deep learning-based segmentation models. Given the high cost of manual labeling and the need for privacy protection, it is often challenging to annotate the testing (target) domain data for model fine-tuning or to collect data from different domains to train domain generalization models. Therefore, using only unlabeled target domain data for test-time adaptation (TTA) presents a more practical but challenging solution. To improve the segmentation accuracy of deep learning-based models on unseen datasets, and especially to enhance the efficiency and stability of TTA for individual samples from heterogeneous domains. In this study, we proposed to dynamically adapt a wavelet-VNet (WaVNet) to unseen target domains with a hybrid objective function, based on each unlabeled test sample during the test time. We embedded multiscale wavelet coefficients into a V-Net encoder and adaptively adjusted the spatial and spectral features according to the input, and the model parameters were optimized by three loss functions. We integrated a shape-aware loss to focus on the foreground segmentations, a Refine loss to correct the incomplete and noisy segmentations caused by domain shifts, and an entropy loss to promote the global consistency of the segmentations. We evaluated the proposed method on multidomain liver and prostate segmentation datasets to assess its advantages over other TTA methods. For the source domain model training of the liver dataset, we used 15 3D MR image samples for training and 5 for validation. Correspondingly, for the prostate dataset, we used 22 3D MR image samples for training and 7 for validation. In the target domain, we used a single 3D MR image sample for adaptation and testing. The total number of testing samples is 60 in the liver dataset (for 3 different domains) and 116 in the prostate dataset (for 6 different domains). The proposed method showed the highest segmentation accuracy among all methods, achieving a mean(±SD) Dice coefficient (DSC) of 78.10±5.23% and a mean 95th Hausdorff distance (HD95) of 15.52±5.84mm on the liver dataset; and a mean DSC of 80.02±3.89% and a mean HD95 of 9.18±3.47mm on the prostate dataset. The DSC is 11.67% (in absolute terms) and 15.27% higher than that of the baseline (no adaptation) method, for the liver and the prostate datasets, respectively. The proposed adaptive WaVNet enhanced the image segmentation accuracy from unseen domains during the test time via unsupervised learning and multi-objective optimization. It can benefit clinical applications where data are scarce or with changing data distributions, including online adaptive radiotherapy. The code will be released at: https://github.com/sanny1226/WaVNet.

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

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

A source free robust domain adaptation approach with pseudo-labels uncertainty estimation for rolling bearing fault diagnosis under limited sample conditions

As essential components of machinery equipment, rolling bearings directly affect the safety of the machinery equipment. The timely diagnosis of bearing faults can effectively prevent equipment lapses. However, bearings are often inconsistently distributed. This has resulted in a significant decrease in their availability. Moreover, the performances of traditional models are poor when fault samples are scarce. The unsupervised domain adaptation (UDA) model based on the transfer learning theory can solve the above problems in static scenarios. However, source domain data are often not directly accessible for privacy protection. Therefore, achieving the robustness of UDA models is significantly challenging. Source-free UDA can achieve a positive transfer from the source domain to the target domain based only on a pretrained source-domain model and unlabeled target-domain data. In this study, we built a source-free robust UDA approach with pseudo-label uncertainty estimation (SFRDA-PLUE) for diagnosing bearing faults using a limited number of samples. First, we designed a robust feature extractor (SANet) and proposed a novel binary soft-constrained information entropy. This was applied to solve the problem that standard information entropy cannot effectively estimate the uncertainty of pseudo-labels. In addition, we constructed a weighted comparison filter strategy to smoothen the fuzzy samples. Finally, we introduced an information-maximizing loss strategy to optimize the performance of the source domain classifier and the pseudo-label estimator. Thus, the robustness of the pseudo-label uncertainty estimation was significantly improved. The experimental results validated that the SFRDA-PLUE approach can achieve excellent diagnostic performance under a limited number of samples.

UDANet: An unsupervised domain adaptive vehicle density estimation network based on joint adversarial learning

Unsupervised domain adaptive vehicle density estimation aims to transfer the knowledge learned from the labeled source domain to the unlabeled target domain, which has received extensive attention due to its practicality and effectiveness. However, the existing methods often rely on a large amount of expensive labeled data, and ignore the large differences between different data domains, making the network trained in one scene difficult to apply to the target scene. To solve these problems, this paper proposes an unsupervised domain adaptive vehicle density estimation network based on joint adversarial learning (UDANet), which effectively extends the model to unlabeled target domain data by adding two domain adaptive components to the pre-trained backbone. Specifically, a multi-level feature discriminator is designed to align vehicle features from the image level and refine features from the pixel level by joint adversarial learning, thus achieving cross-domain transfer of features from the source domain to the target domain. In addition, to address the shared convolutional structure that incorrectly highlights irrelevant information, a feature correction module is designed to improve the adaptability of the network to target domain data by re-modeling the source and target data according to channel independence. Several well-known backbones are used to prove the superiority of the backbone and the mobility of the two domain adaptive components. The domain adaptation experimental results on four vehicle datasets show that UDANet achieves high estimation accuracy compared to current state-of-the-art unsupervised methods, and is suitable for the task of vehicle density estimation in complex scenes.

A dynamics simulation-assisted transfer learning method for track condition diagnosis in urban rail transits

Track defects are gradually emerging with the development of urban rail transits. However, there is rare research implemented to diagnose track conditions in real time. Although some intelligent data-driven methods seem to have the potential to achieve the track condition diagnosis, it’s hard to acquire sufficient labeled data in actual applications. This study proposes a dynamics simulation-assisted transfer learning (TL) method for label-scarce track condition diagnosis. Firstly, a dynamics model of axle-box bearings considering the service environment is established. Based on this model, a large amount of axle-box vibration signals corresponding to healthy/defective track conditions is simulated. Wavelet transform is performed for these signals to characterize their time-frequency energy distribution modes in the format of time-frequency maps, which are considered source-domain data. Similarly, the time-frequency maps of the collected signals during vehicle operation are served as the target-domain data. Subsequently, a sub-domain alignment TL network is constructed to map the data from the source and target domain into a deep feature space. In this network, unlabeled target-domain data are classified to obtain their pseudo labels. Finally, Wasserstein distance measure and multiple domain discriminators are employed to achieve label alignment between two domains for each corresponding category. A feature centroid-driven loss function is applied to further reduce the intra-class variations, ultimately realizing accurate knowledge transfer from simulated signals to collected signals. A two-level sliding window algorithm is designed to detect abnormal axle-box vibration signal parts which are then diagnosed through the well-trained network. The proposed method is validated through a transfer diagnosis experiment using simulated signals and collected signals. This study provides a promising solution to diagnose different track conditions, which is of great significance for ensuring running safety in urban rail transits.

Source-free domain adaptation via dynamic pseudo labeling and Self-supervision

Recently, unsupervised domain adaptation (UDA) has attracted extensive interest in relieving the greedy requirement of vanilla deep learning for labeled data. It seeks for a solution to adapt the knowledge from a well-labeled training dataset (source domain) to another unlabeled target dataset (target domain). However, in some practical scenarios, the source domain data is inaccessible for a variety of reasons, and only a model trained on it can be provided, thus deriving a more challenging task, i.e., source-free unsupervised domain adaptation (SFUDA). Some pseudo labeling-based methods have been proposed to solve it by predicting pseudo labels for the unlabeled target domain data. Nevertheless, incorrectly designated pseudo labels will impose an adverse impact on the network adaptation. To alleviate this issue, we propose a dynamic confidence-based pseudo labeling strategy for SFUDA in this paper. Unlike those methods that first rigidly assign pseudo labels to all target domain data and then try to weaken the effect of incorrect pseudo labels in training, we proactively label the target samples with higher confidence in a dynamic manner. To further relieve the impact of incorrect pseudo labels, we harness the collaborative learning to constrain the consistency of the network and impose an additional soft supervision. Besides, we also investigate the possible problem brought by our labeling strategy, i.e., the neglect of wavering samples near the decision boundary, and solve it by injecting the self-supervised learning into our model. Experiments on three UDA benchmark datasets demonstrate the state-of-the-art performance of our proposed method. The code is publicly available at https://github.com/meowpass/DPLS.

Regularized joint self-training: A cross-domain generalization method for image classification

Unsupervised domain adaptive (UDA) is a widely used approach in machine learning strategies that helps move information from a source domain with rich supervised data to a target domain with limited supervised data. In recent years, self-training derived from semi-supervised learning provides a powerful tool for unsupervised domain adaptive by training unlabeled target domain data using pseudo-tags. However, in the case of domain shift, the pseudo-labels generated by standard self-training are no longer reliable due to the influence of the class-distribution shift in a specific domain, resulting in the noise of the pseudo-label error blocking the upper bound of the accuracy. To solve these problems, a domain adaptive method based on regularization joint self-training (RJS) is proposed in this paper. RJS consists of two key steps: First, the generation and optimization of pseudo-tags are trained jointly on the source domain to achieve automatic cross-domain generalization of pseudo-tags. Secondly, the regularization method is introduced as a confidence rule for false labels in initial model training, which implicitly prevents the model from remembering false labels. To verify the effectiveness of the proposed method, experiments were performed on VisDA-2017, Office-31, Office-Home, and DomainNet datasets, and the accuracy rates reached 87.1%, 89.3%, 76.9%, and 57.3%, respectively. In addition, the average classification performance of RJS on VisDA-2017 improved by about 12.6% compared to FixMatch, the standard self-training method. Experimental results demonstrate that RJS achieves state-of-the-art performance on multiple standard UDA benchmarks and confirm its effectiveness in the presence of domain-specific class distribution shifts.

Fourier Domain Adaptation for the Identification of Grape Leaf Diseases

With the application of computer vision in the field of agricultural disease recognition, the convolutional neural network is widely used in grape leaf disease recognition and has achieved remarkable results. However, most of the grape leaf disease recognition models have the problem of weak generalization ability. In order to overcome this challenge, this paper proposes an image identification method for grape leaf diseases in different domains based on Fourier domain adaptation. Firstly, Fourier domain adaptation is performed on the labeled source domain data and the unlabeled target domain data. To decrease the gap in distribution between the source domain data and the target domain data, the low-frequency spectrum of the source domain data and the target domain data is swapped. Then, three convolutional neural networks (AlexNet, VGG13, and ResNet101) were used to train the images after style changes and the unlabeled target domain images were classified. The highest accuracy of the three networks can reach 94.6%, 96.7%, and 91.8%, respectively, higher than that of the model without Fourier transform image training. In order to reduce the impact of randomness, when selecting the transformed image, we propose using farthest point sampling to select the image with low feature correlation for the Fourier transform. The final identification result is also higher than the accuracy of the network model trained without transformation. Experimental results showed that Fourier domain adaptation can improve the generalization ability of the model and obtain a more accurate grape leaf disease recognition model.

Predicting Clinical Anticancer Drug Response of Patients by using Domain Alignment and Prototypical Learning.

Anticancer drug response prediction is crucial in developing personalized treatment plans for cancer patients. However, High-quality patient anticancer drug response data are scarce and cell line data and patient data have different distributions, models trained solely on cell line data perform poorly. Some existing methods predict anticancer drug response by transferring knowledge from the cell line domain to the patient domain using transfer learning. However, the robustness of these classifiers is affected by anomalies in the cell line data, and they do not utilize the knowledge in the unlabeled target domain data. To this end, we proposed a model called DAPL to predict patient responses to anticancer drugs. The model extracts domain-invariant features from cell lines and patients by constructing multiple VAEs and extracts drug features using GNNs. These features are then combined for prototypical learning to train a classifier, resulting in better predictions of patient anticancer drug response. We used the cell line datasets CCLE and GDSC as source domains and the patient datasets TCGA and PDTC as target domains and conducted experiments. The results indicate that DAPL shows excellent performance in predicting patient anticancer drug response compared to other state-of-the-art methods.

Adaptive Stability Contingency Screening for Operational Planning Based on Domain-Adversarial Graph Neural Network

Compared to contingency screening (CS) techniques that focus on thermal/voltage limit violation, CS that addresses the transient and small-signal stability contingencies in the existing references requires a higher computational cost, limiting its use to off-line contingency analysis. Therefore, it poses the challenge of developing an effective CS scheme for fast recognition of critical contingency, which is one of the major concerns of system operators. To deal with this problem, this paper proposes an adaptive stability CS (SCS) scheme for operational planning. One challenge in developing the adaptive SCS for different cardinal points is data distribution discrepancy resulted from load/topology changes. To align data distribution between different domains (i.e., cardinal points), a domain-adversarial graph neural network (DAGNN) is developed to learn the domain-invariant features, so that the DAGNN model trained on the labeled source domain (e.g., peak cardinal point 2B) data can be applied to the unlabeled target domain (e.g., trough cardinal point 1B) data for SCS. To make the proposed SCS more efficient in dealing with power system data, a graph learning approach combined with graph transformer and graph isomorphism network is used in DAGNN to provide feature representations considering the graph properties of power systems, where nodes and edges refer to buses and transmission lines, respectively. Experiments on IEEE 39 Bus system and IEEE 118 Bus system have verified the effectiveness of the proposed model.

Unsupervised Domain-Adaptive Object Detection via Localization Regression Alignment.

Unsupervised domain-adaptive object detection uses labeled source domain data and unlabeled target domain data to alleviate the domain shift and reduce the dependence on the target domain data labels. For object detection, the features responsible for classification and localization are different. However, the existing methods basically only consider classification alignment, which is not conducive to cross-domain localization. To address this issue, in this article, we focus on the alignment of localization regression in domain-adaptive object detection and propose a novel localization regression alignment (LRA) method. The idea is that the domain-adaptive localization regression problem can be transformed into a general domain-adaptive classification problem first, and then adversarial learning is applied to the converted classification problem. Specifically, LRA first discretizes the continuous regression space, and the discrete regression intervals are treated as bins. Then, a novel binwise alignment (BA) strategy is proposed through adversarial learning. BA can further contribute to the overall cross-domain feature alignment for object detection. Extensive experiments are conducted on different detectors in various scenarios, and the state-of-the-art performance is achieved; these results demonstrate the effectiveness of our method. The code will be available at: https://github.com/zqpiao/LRA.

Time-frequency supervised contrastive learning via pseudo-labeling: An unsupervised domain adaptation network for rolling bearing fault diagnosis under time-varying speeds

The varying speed can cause the significant data distribution shift of bearings, making it difficult for deep learning-based bearing fault diagnosis models to ensure good generalization. Domain adaptation methods have been developed to address the domain shifts, while they struggle with the class-invariant features extraction under variable speed. Accordingly, a time–frequency supervised contrastive learning framework (TF-SupCon) is proposed for unsupervised cross-speed fault diagnosis of bearing. TF-SupCon adopts a pre-training-downstream task framework that aims to extract speed immune class-invariant features. During the pre-training phase, the physical consistency of the time-domain information and the frequency-domain information of bearing signal, a general feature applicable to different speed conditions, is learned in a targeted manner through supervised contrastive learning. Additionally, a K-Nearest Neighbor (KNN) algorithm based on cosine distance is designed to assign pseudo-labels to unlabeled target domain data, enabling cross-domain supervised contrastive pre-training. In the downstream task, unsupervised cross-domain fault diagnosis is performed using a KNN classifier and the speed immune time–frequency features by the trained encoders. It is worth noting that the same metric is maintained throughout both the pre-training phase and the downstream task, ensuring organic connection between the two stages. The superiority of TF-SupCon was demonstrated through a variety of fault diagnosis experiments conducted on both public and self-collected datasets. Lastly, the distances between time-domain and frequency-domain features of different categories were studied to verify the physical consistency between the features.

Contrasting augmented features for domain adaptation with limited target domain data

Domain adaptation aims to alleviate distribution gaps between source and target domains. However, when the available target domain data are scarce for training, learning generalizable representations for domain adaptation is challenging. We propose a novel approach, dubbed Contrasting Augmented Features (CAF), to tackle the challenge of insufficient target domain data for domain adaptation, by generating and contrasting augmented features. We introduce a semantic feature generator to generate augmented features by replacing the instance-level feature statistics of one domain with another domain. With the augmented features, we further design the reweighted instance contrastive loss and category contrastive loss to improve feature discrimination and align feature distributions of source and target domains. CAF can be applied to few-shot domain adaptation and unsupervised domain adaptation with limited unlabeled target domain data. Despite its simplicity, extensive experiments show promising results for both applications. In addition, experiments demonstrate that CAF is more robust to the number of target domain data and also effective in vanilla unsupervised domain adaptation setting with full target domain data.

Cluster-based Dual-branch Contrastive Learning for unsupervised domain adaptation person re-identification

Unsupervised domain adaptation (UDA) person re-identification (Re-ID) is to enhance the discriminability of Re-ID tasks in the target domain by leveraging labeled source domain data and unlabeled target domain data. Employing clustering as a mean to generate pseudo labels has emerged as a successful approach for addressing UDA Re-ID tasks. However, clustering may be disturbed by data noise when generating pseudo labels, resulting in unstable quality of pseudo labels. In addition, persons with similar colored clothing can also affect the recognition accuracy of the model. To address these aforementioned issues in UDA Re-ID, we propose a Cluster-based Dual-branch Contrastive Learning (CDCL) approach. In CDCL, we design partially grayed images as input and utilize two-branch network structure to reduce the effect of upper-body clothing color on model recognition. Based on the two-branch network architecture, we additionally propose a cluster refinement method to improve the confidence of pseudo labels, which selects samples with high similarity, i.e., samples with weighted distance between subclass samples less than a threshold, and thus makes pseudo labels more reliable. The proposed CDCL has shown its effectiveness through experimental results, achieving an mAP accuracy of 81.5%.

Research on a small sample feature transfer method for fault diagnosis of reciprocating compressors

The assumption of a data-driven fault diagnosis model is that both the training data and the test data come from the same probability distribution. In the diagnosis of reciprocating compressors, problems such as sensor failures and changes in installation locations often occur, making it difficult to obtain effective, stable and sufficient data. The sample data is not sufficient to meet the requirements of the fault diagnosis model. We propose a fault diagnosis method for reciprocating compressors based on feature transfer at small sample sizes. First, the sample data is projected to the Regenerated Kernel Hilbert Space (RKHS) using Transfer Component Analysis (TCA). Second, the minimization of distributional differences across domains is measured by the maximum mean difference (MMD). Then, combined with the adaptive feature extraction capability of Deep Belief Network (DBN), the already labeled source domain and unlabeled target domain data are aggregated during the training phase to diagnose faults. This approach uses a novel optimized learning approach, free energy in persistent contrastive divergence, in DBN learning and training. It solves the problem of DBN classification ability degradation in long-term training. Finally, a low-power single-acting reciprocating compressor experimental platform is built to carry out fault diagnosis experiments. The research results show that better diagnostic accuracy can be obtained by reducing the distribution difference of source and target domain data in projected high-dimensional RKHS through the common transfer components learned by TCA. Compared with traditional small-sample machine learning algorithms and DBN models, this approach has strong generalization ability, and the accuracy rate is as high as 92%. This study proves the superiority of both transfer learning (TL) and deep learning fault diagnosis models in solving the problem of less fault data and provides a theoretical reference for accurate fault diagnosis of complex dynamic equipment in the case of small samples.

Cross-Domain Automatic Modulation Classification Using Multimodal Information and Transfer Learning

Automatic modulation classification (AMC) based on deep learning (DL) is gaining increasing attention in dynamic spectrum access for 5G/6G wireless communications. However, inconsistent feature parameters between the training (source) and testing (target) data lead to performance degradation or even failure of existing DL-based AMC. The primary reason for this is the difficulty in obtaining sufficient labeled training data in the target domain. Therefore, we propose a novel cross-domain AMC algorithm based on multimodal information and transfer learning, utilizing abundant unlabeled target domain data. We achieve complementary gains by fusing multimodal information such as amplitude, phase, and spectrum, which are used to train a network. Additionally, we apply domain adversarial neural network technology from transfer learning to learn from a large number of unlabeled data samples in the target domain to address the issue of decreased accuracy in cross-domain AMC caused by differences in sampling rate, signal-to-noise ratio, and channel variations. Furthermore, we introduce class weight weighting and entropy weighting to solve the partial domain adaptation problem, considering that the target domain has fewer modulation signal classes than the source domain. Experimental results on two designed modulation datasets demonstrate improved performance gains, thus validating the effectiveness of the proposed method.

Cyclically Disentangled Feature Translation for Face Anti-spoofing

Current domain adaptation methods for face anti-spoofing leverage labeled source domain data and unlabeled target domain data to obtain a promising generalizable decision boundary. However, it is usually difficult for these methods to achieve a perfect domain-invariant liveness feature disentanglement, which may degrade the final classification performance by domain differences in illumination, face category, spoof type, etc. In this work, we tackle cross-scenario face anti-spoofing by proposing a novel domain adaptation method called cyclically disentangled feature translation network (CDFTN). Specifically, CDFTN generates pseudo-labeled samples that possess: 1) source domain-invariant liveness features and 2) target domain-specific content features, which are disentangled through domain adversarial training. A robust classifier is trained based on the synthetic pseudo-labeled images under the supervision of source domain labels. We further extend CDFTN for multi-target domain adaptation by leveraging data from more unlabeled target domains. Extensive experiments on several public datasets demonstrate that our proposed approach significantly outperforms the state of the art. Code and models are available at https://github.com/vis-face/CDFTN.