Domain-invariant Knowledge Research Articles

SL: Stable Learning in Source-Free Domain Adaptation for Medical Image Segmentation

Deep learning techniques for medical image analysis often encounter domain shifts between source and target data. Most existing approaches focus on unsupervised domain adaptation (UDA). However, in practical applications, many source domain data are often inaccessible due to issues such as privacy concerns. For instance, data from different hospitals exhibit domain shifts due to equipment discrepancies, and data from both domains cannot be accessed simultaneously because of privacy issues. This challenge, known as source-free UDA, limits the effectiveness of previous UDA medical methods. Despite the introduction of various medical source-free unsupervised domain adaptation (MSFUDA) methods, they tend to suffer from an over-fitting problem described as “longer training, worse performance”. To address this issue, we proposed the Stable Learning (SL) strategy. SL is a method that can be integrated with other approaches and consists of weight consolidation and entropy increase. Weight consolidation helps retain domain-invariant knowledge, while entropy increase prevents over-learning. We validated our strategy through experiments on three MSFUDA methods and two public datasets. For the abdominal dataset, the application of the SL strategy enables the MSFUDA method to effectively address the domain shift issue. This results in an improvement in the Dice coefficient from 0.5167 to 0.7006 for the adaptation from CT to MRI, and from 0.6474 to 0.7188 for the adaptation from MRI to CT. The same improvement is observed with the cardiac dataset. Additionally, we conducted ablation studies on the two involved modules, and the results demonstrated the effectiveness of the SL strategy.

Learning Domain Specific Sub-layer Latent Variable for Multi-Domain Adaptation Neural Machine Translation

Domain adaptation proves to be an effective solution for addressing inadequate translation performance within specific domains. However, the straightforward approach of mixing data from multiple domains to obtain the multi-domain neural machine translation (NMT) model can give rise to the parameter interference between domains problem, resulting in a degradation of overall performance. To address this, we introduce a multi-domain adaptive NMT method aimed at learning domain specific sub-layer latent variable and employ the Gumbel-Softmax reparameterization technique to concurrently train both model parameters and domain specific sub-layer latent variable. This approach facilitates the learning of private domain-specific knowledge while sharing common domain-invariant knowledge, effectively mitigating the parameter interference problem. The experimental results show that our proposed method significantly improved by up to 7.68 and 3.71 BLEU compared with the baseline model in English-German and Chinese-English public multi-domain datasets, respectively.

A Domain Adaptive Semantic Segmentation Method Using Contrastive Learning and Data Augmentation

For semantic segmentation tasks, it is expensive to get pixel-level annotations on real images. Domain adaptation eliminates this process by transferring networks trained on synthetic images to real-world images. As one of the mainstream approaches to domain adaptation, most of the self-training based domain adaptive methods focus on how to select high confidence pseudo-labels, i.e., to obtain domain invariant knowledge indirectly. A more direct means to explicitly align the data of the source and target domains globally and locally is lacking. Meanwhile, the target features obtained by traditional self-training methods are relatively scattered and cannot be aggregated in a relatively compact space. We offer an approach that utilizes data augmentation and contrastive learning in this paper to perform more effective knowledge migration with the basis of self-training. Specifically, the style migration and image mixing modules are first introduced for data augmentation to cope with the problem of large domain gaps in the source and target domains. To assure the aggregation of features from the same class and the discriminability of features from other classes during the training process, we propose a multi-scale pixel-level contrastive learning module. What’s more, a cross-scale contrastive learning module is proposed to help each level of the model gain the capability to obtain more information on the basis of its own original task. Experiments show that our final trained model can effectively classify the images from target domain.

Multi-source domain adversarial graph convolutional networks for rolling mill health states diagnosis under variable working conditions

As the rolling mill often encounters variable and complicated working conditions and shock loads, unsupervised domain adaptive (UDA) methods are imperative in its health monitoring. However, efforts of applying UDA methods on the rolling mill are negligible, and many existing approaches have constraints in domain adaptation, domain label, and data construction that prevent meaningful features from being extracted. Hence, a multi-source domain adversarial graph convolutional networks framework (MSDAGCNs) is presented to overcome these challenges and combine three essential elements to achieve cross-domain health states diagnosis under variable working conditions. First, a shared feature extract module is introduced to extract common features. Then, the features are input to a multi-source feature extract module to extract the data construction from the graphs generated by a graph construction module. Meanwhile, a multi-source domain adversarial classifier module is modeled to extract multi-source invariant features and classify them. After that, the local maximum mean discrepancy is employed to align the domain categories. Next, a task classifier module integrates the results of the multi-source classifier for reliable health state diagnosis. Results on the two cases can verify that the proposed MSDAGCNs can not only outperform other state-of-the-art methods, but also extract domain-invariant knowledge. Compared with the best-performing method, the proposed method can boost accuracy by 0.53% and 0.83% in the simplest task of the two case studies, respectively. Furthermore, the arrangement of sensors on the rolling mill is discussed to select the optimal location for collecting vibrations.

Cross-scenario capacity estimation for lithium-ion batteries via knowledge query domain mixing-up network

Introduction: Deep learning has demonstrated exceptional prowess in estimating battery capacity. However, its effectiveness is often compromised by performance degradation under a consequence of varying operational conditions and diverse charging/discharging protocols.Methods: To tackle this issue, we introduce the Knowledge Query Domain Mixing-up Network (KQDMN), a domain adaptation-based solution adept at leveraging both domain-specific and invariant knowledge. This innovation enriches the informational content of domain features by segregating the functions of feature extraction and domain alignment, enhancing the efficacy of KQDMN in utilizing diverse knowledge types. Moreover, to identify time-deteriorating features in battery time series data, we employ convolutional operations. These operations are pivotal in extracting multi-scale features from the battery's characteristic curves. Inspired by the Transformer model, we have developed a set of knowledge queries that integrate these multi-scale features seamlessly, thereby enabling extensive global feature extraction. To ensure the retention of domain-specific information, we have instituted two independent feature extraction pathways. Pursuing domain-invariant knowledge, this study introduces cross-attention as a mechanism to connect two domain spaces, effectively diminishing the disparity between source and target distributions.Results and Discussion: This approach is crucial for accurately estimating capacity in batteries with diverse performance characteristics. The practicality and robustness of the proposed method are validated using the MIT battery aging dataset, yielding highly satisfactory outcomes. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2) for our capacity estimation process are 0.19%, 0.23%, and 0.997, respectively, highlighting the precision and reliability of our approach.

Locality Cross-domain Discriminant Analysis for Membranous Nephropathy Recognition Using Microscopic Hyperspectral Imaging.

Cross-domain methods have been proposed to learn the domain invariant knowledge that can be transferred from the source domain to the target domain. Existing cross-domain methods attempt to minimize the distribution discrepancy of the domains. However, these methods fail to explore the domain invariant subspace due to the samples of different classes between two domains may overlap in the new subspace. They consider the features in the original space data that may be unnecessary or irrelevant to the final classification, and neglect to preserve the local manifold structure between two domains. To solve these problems, a novel feature extraction method called Locality Cross-domain Discriminant Analysis (LCDA) is proposed. LCDA first aligns the distributions and avoids overlap between two domains. Then, LCDA exploits the local manifold structure to maintain the discriminative capability of the low-dimensional projection matrices. Finally, a robust constraint is utilized to preserve the robustness of the projection matrices. The proposed LCDA not only avoids overlap between different classes but also explores the local manifold information. Experiment results on the medical membranous nephropathy hyperspectral dataset demonstrate that the proposed LCDA has better performance than other relevant feature extraction methods.

An End-to-end Domain Generalization Fault Diagnosis Method Based on Adaptive Weighted Domain Adversarial Learning

Domain adaptation (DA) based fault diagnosis methods have attracted great attention for a long time, which leverages domain-invariant knowledge from a source domain (SD) to a special target domain (TD). However, in practise, there not usually exists only single SD and a specific and available TD. Mostly, data is generated from multiple domain distributions due to varied working conditions, and the TD is not always available during model training. Hence, how to use these multiple domain distribution data to build a model to generalize domain knowledge to an “unseen” TD, is an emerging and challengeable issue. In this paper, a novel domain generalization (DG) based fault diagnosis methods is proposed. Multiple domains are considered jointly to extract generalized domain-invariant features. A multiple residual blocks structure is adopted as the main feature extractor, an additional extractor is added to extract prior knowledge from statistic features. Besides, a k-means based adaptive weighted domain adversarial learning is designed to realize multiple domain confusion in a latent space while the wasserstein distance is calculated to assist in narrowing discrepancy between different domain distributions. Experiments of multiple domain distributions tasks from two datasets are conducted to evaluate the proposed method, and the results have verified the effectiveness of proposed method.

Class-Incremental Unsupervised Domain Adaptation via Pseudo-Label Distillation.

Class-Incremental Unsupervised Domain Adaptation (CI-UDA) requires the model can continually learn several steps containing unlabeled target domain samples, while the source-labeled dataset is available all the time. The key to tackling CI-UDA problem is to transfer domain-invariant knowledge from the source domain to the target domain, and preserve the knowledge of the previous steps in the continual adaptation process. However, existing methods introduce much biased source knowledge for the current step, causing negative transfer and unsatisfying performance. To tackle these problems, we propose a novel CI-UDA method named Pseudo-Label Distillation Continual Adaptation (PLDCA). We design Pseudo-Label Distillation module to leverage the discriminative information of the target domain to filter the biased knowledge at the class- and instance-level. In addition, Contrastive Alignment is proposed to reduce domain discrepancy by aligning the class-level feature representation of the confident target samples and the source domain, and exploit the robust feature representation of the unconfident target samples at the instance-level. Extensive experiments demonstrate the effectiveness and superiority of PLDCA.

A Latent Representation Generalizing Network for Domain Generalization in Cross-Scenario Monitoring.

Cross-scenario monitoring requires domain generalization (DG) for changed knowledge when auxiliary information is unavailable and only one source scenario is involved. In this article, a latent representation generalizing network (LRGN) is proposed to learn transferable knowledge through generalizing the latent representations for cross-scenario monitoring in perimeter security. LRGN is composed of a sequential-variational generative adversarial network (SVGAN), a coupled SVGAN (Co-SVGAN), and a knowledge-aggregated SVGAN. First, the Co-SVGAN can learn domain-invariant latent representations to model dual-domain joint distribution of background data, which is usually sufficient in the source and target scenarios. Deceptive domain shifts are generated based on the domain-invariant latent representations without auxiliary information. Then, SVGAN models the changing knowledge by estimating the distribution of domain shifts. Furthermore, the knowledge-aggregated SVGAN can transfer the learned domain-invariant knowledge from Co-SVGAN for generalizing the latent representations through approximating the distribution of domain shifts. Accordingly, LRGN is trained by a four-phase optimization strategy for DG through generating target-scenario samples of concerned events based on the generalized latent representations. The feasibility and effectiveness of the proposed method are validated through real-field experiments of perimeter security applications in two scenarios.

LC-MSM: Language-Conditioned Masked Segmentation Model for unsupervised domain adaptation

Unsupervised domain adaptation (UDA) is an important research topic in semantic segmentation tasks, wherein pixel-wise annotations are often difficult to collect in a test environment due to their high labeling costs. Previous UDA-based studies trained their segmentation networks using labeled synthetic data and unlabeled realistic data as source and target domains, respectively. However, they often fail to distinguish semantically similar classes, such as person vs. rider and road vs. sidewalk, because these classes are prone to confusion in domain-shifted environments. In this paper, we introduce a Language-Conditioned Masked Segmentation Model (LC-MSM), which is a new framework for the joint learning of context relations and domain-agnostic information for domain-adaptive semantic segmentation. Specifically, we reconstruct semantic labels with masked image conditions on the generalized text embeddings of the corresponding semantic class from OpenCLIP, which contains domain-invariant knowledge from large-scale data. To this end, we correlate the generalized text embeddings onto the per-pixel image feature of a masked image that learned the spatial context to further append domain-agnostic language information to the semantic decoder. This facilitates the generalization of our model to the target domain via the learning of context information within individual training instances, while considering cross-domain representations spanning the entire dataset. LC-MSM achieves an unprecedented UDA performance of 71.8 and 62.8 mIoU on GTA→Cityscapes and SYNTHIA→Cityscapes, respectively, which corresponds to an improvement of +3.5 and +1.9 percent points over the baseline method.

Increasing Robustness of Data-Driven Fault Diagnostics with Knowledge Graphs

In the realm of PHM, it is common to possess not only process data but also domain knowledge, which, if integrated into data-driven algorithms, can aid in solving specific tasks.This paper explores the integration of knowledge graphs (KGs) into deep learning models to develop a more resilient approach capable of handling domain shifts, such as variations in machine operation conditions.We present and assess a KG-enhanced deep learning approach in a representative PHM use case, demonstrating its effectiveness by incorporating domain-invariant knowledge through the KG.Furthermore, we provide guidance for constructing a comprehensive hierarchical KG representation that preserves semantic information while facilitating numerical representation.The experimental results showcase the improved performance and domain shift robustness of the KG-enhanced approach in fault diagnostics.

Adaptive and Simultaneous Trajectory Prediction for Heterogeneous Agents via Transferable Hierarchical Transformer Network

Simultaneously and accurately predicting trajectories of multiple heterogeneous agents is crucial for intelligent transportation systems (ITS) applications, e.g., connected and autonomous vehicles. Existing model-based and data-driven methods can achieve good prediction accuracy, but most of them neglect the domain shift issue and prevalent imperfect data problems, i.e., few-shot learning and zero-shot learning issues. To address these issues, we propose a multi-source transfer learning (TL) framework, transferable hierarchical Siamese Transformer network (T-HSTN), for trajectory prediction of multiple heterogeneous agents, e.g., vehicles, bicycles, and pedestrians, at urban unsignalized intersections under small data conditions. Specifically, by extending the self-attention mechanism and exploring feature representations of traffic scenes, a Transformer-based network that hierarchically extracts temporal/spatial features and map features is introduced as the basic prediction model. Moreover, a TL framework with adaptive learning and feature alignment modules is built to explore the feature representations of unfixed traffic scenes and align both statistical and deep features to learn domain-invariant knowledge. More challenging trajectory prediction experiments are designed, corresponding to newly-built or badly-instrumented intersections under real-world scenarios. Experimental results verify the proposed method’s high accuracy, transferability, and generability. Our work fills the gap in solutions and benchmarks for TL tasks in trajectory prediction for heterogeneous agents. The conducted TL experiments provide a more practical setting of considering imperfect data problems in trajectory prediction.

Cross-Domain Aspect-Based Sentiment Classification by Exploiting Domain- Invariant Semantic-Primary Feature

Aspect-based sentiment analysis is an important task in fine-grained sentiment analysis, which aims to infer the sentiment towards a given aspect. Previous studies have shown notable success when sufficient labeled training data is available. However, annotating adequate data is labor-intensive, which sets substantial barriers for generalizing the sentiment predictor to the new domain. Two main challenges exist in cross-domain aspect-based sentiment analysis. One challenge is acquiring the domain-invariant knowledge; the other challenge is mining the syntactic-related words towards the aspect-term. In this paper, we propose a transformer-based semantic-primary knowledge transferring network (TSPKT) for cross-domain aspect-term sentiment analysis, which utilizes semantic-primary knowledge as a bridge to enable knowledge transfer across domains. Specifically, we first build an S-Graph from external semantic lexicons, and extract the semantic-primary knowledge from the S-Graph. Second, AoaGraphormer is proposed to learn the syntactically relevant words towards the aspect-term. Third, we extend the standard biLSTM classifier to fully integrate the semantic-primary knowledge by adding a novel knowledge-aware memory unit (KAMU) to the biLSTM cell. Extensive experiments on six cross-domain setups demonstrate the superiority of TSPKT against the state-of-the-art baseline methods.

SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud

LiDAR-based 3D object detection is an indispensable task in advanced autonomous driving systems. Though impressive detection results have been achieved by superior 3D detectors, they suffer from significant performance degeneration when facing unseen domains, such as different LiDAR configurations, different cities, and weather conditions. The mainstream approaches tend to solve these challenges by leveraging unsupervised domain adaptation (UDA) techniques. However, these UDA solutions just yield unsatisfactory 3D detection results when there is a severe domain shift, e.g., from Waymo (64-beam) to nuScenes (32-beam). To address this, we present a novel Semi-Supervised Domain Adaptation method for 3D object detection (SSDA3D), where only a few labeled target data is available, yet can significantly improve the adaptation performance. In particular, our SSDA3D includes an Inter-domain Adaptation stage and an Intra-domain Generalization stage. In the first stage, an Inter-domain Point-CutMix module is presented to efficiently align the point cloud distribution across domains. The Point-CutMix generates mixed samples of an intermediate domain, thus encouraging to learn domain-invariant knowledge. Then, in the second stage, we further enhance the model for better generalization on the unlabeled target set. This is achieved by exploring Intra-domain Point-MixUp in semi-supervised learning, which essentially regularizes the pseudo label distribution. Experiments from Waymo to nuScenes show that, with only 10% labeled target data, our SSDA3D can surpass the fully-supervised oracle model with 100% target label. Our code is available at https://github.com/yinjunbo/SSDA3D.

Uni3DA: Universal 3D Domain Adaptation for Object Recognition

Traditional 3D point cloud classification tasks focus on training a classifier in the closed-set scenario, where training and test data have the same label set and the same data distribution. In this work, we focus on a more challenging and realistic scenario in 3D point cloud classification task: universal domain adaptation (UniDA), where 1) data distributions for training and test data are different; and 2) for given label sets of training data and test data, they may have the shared classes and keep the private classes respectively, introducing an extra label set discrepancy. To solve UniDA problem, researchers have designed many methods based on 2D image datasets. However, due to the difficulty in capturing discriminative local geometric structures brought by the unordered and irregular 3D point cloud data, we cannot directly deploy the existing methods based on 2D image datasets to the 3D scenarios. To address UniDA in 3D scenarios, we develop a 3D universal domain adaptation framework, which consists of three modules: Self-Constructed Geometric (SCG) module, Local-to-Global Hypersphere Reasoning (LGHR) module and Self-Supervised Boundary Adaptation (SBA) module. SCG and LGHR generate the discriminative representation, which is used to acquire domain-invariant knowledge for training and test data. SBA is designed to automatically recognize whether a given label is from the shared label set or private label set, and adapts training and test data from the shared label set. To our best knowledge, this work is the first exploration of UniDA for 3D scenarios. Extensive experiments on public 3D point cloud datasets verify that the proposed method outperforms the existing UniDA methods.

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.

Partial Alignment for Object Detection in the Wild

Conventional object detectors often encounter remarkable performance drops due to the domain shift caused by environmental changes. However, labeling sufficient training data drawn from various domains is cost-ineffective and labor-intensive. To this end, unsupervised domain adaptive object detection (DAOD) has attracted much attention, in which the detector is transferred from the label-rich source domain to a label-agnostic target domain. Most of the existing cross-domain object detectors are designed with a parameter-shared network architecture, which, however, has an inherent flaw to accumulate the source errors caused by the inaccurate target distribution. As a result, the target data instead of decisive source data may dominate the learning towards model collapse. To overcome the risk, we propose an Asymmetric Tri-way Faster-RCNN (ATF) for DAOD tasks, in which a novel ancillary net is deployed with the chief net and it brings two merits. 1) The ancillary net enables the source distribution to be preserved without accumulating source error/distortion, such that the target distribution is dominated by source data and the model collapse is alleviated. 2) Ancillary target features can be generated by the ancillary net, which further enhances the discrimination of the target domain object detector. Furthermore, in order to remove the useless source-specific knowledge and exploit the informative domain-invariant knowledge, we propose a Partial Alignment based ATF (PA-ATF) model, in which an adversarial shuffling based mutual-information minimization strategy for domain disentanglement is provided. Intuitively, transferring only the source-domain invariant information to the target domain is more conscionable. Extensive experiments on benchmark datasets, including the Cityscapes, Foggy Cityscapes, Pascal VOC, Clipart, Watercolor, SIM10K, and KITTI demonstrate the remarkable performance of our models over other state-of-the-art approaches.

A Source-Free Domain Adaptive Polyp Detection Framework With Style Diversification Flow.

The automatic detection of polyps across colonoscopy and Wireless Capsule Endoscopy (WCE) datasets is crucial for early diagnosis and curation of colorectal cancer. Existing deep learning approaches either require mass training data collected from multiple sites or use unsupervised domain adaptation (UDA) technique with labeled source data. However, these methods are not applicable when the data is not accessible due to privacy concerns or data storage limitations. Aiming to achieve source-free domain adaptive polyp detection, we propose a consistency based model that utilizes Source Model as Proxy Teacher (SMPT) with only a transferable pretrained model and unlabeled target data. SMPT first transfers the stored domain-invariant knowledge in the pretrained source model to the target model via Source Knowledge Distillation (SKD), then uses Proxy Teacher Rectification (PTR) to rectify the source model with temporal ensemble of the target model. Moreover, to alleviate the biased knowledge caused by domain gaps, we propose Uncertainty-Guided Online Bootstrapping (UGOB) to adaptively assign weights for each target image regarding their uncertainty. In addition, we design Source Style Diversification Flow (SSDF) that gradually generates diverse style images and relaxes style-sensitive channels based on source and target information to enhance the robustness of the model towards style variation. The capacities of SMPT and SSDF are further boosted with iterative optimization, constructing a stronger framework SMPT++ for cross-domain polyp detection. Extensive experiments are conducted on five distinct polyp datasets under two types of cross-domain settings. Our proposed method shows the state-of-the-art performance and even outperforms previous UDA approaches that require the source data by a large margin. The source code is available at github.com/CityU-AIM-Group/SFPolypDA.

A domain generalization network combing invariance and specificity towards real-time intelligent fault diagnosis

Domain adaptation-based fault diagnosis (DAFD) methods have been explored to address cross-domain fault diagnosis problems, where distribution discrepancy exists between the training and testing data. However, the indispensable priori target distribution needed by DAFD methods hinders their application on real-time cross-domain fault diagnosis, where target data are not accessible in advance. To tackle this challenge, this paper proposes a novel domain generalization network for fault diagnosis under unknown working conditions. The main idea is to exploit domain invariance and retain domain specificity simultaneously, enabling deep models to benefit from the universal applicability of domain-invariant features while retaining the predictive power of specialized domain structures. Global distribution alignment and local class cluster are implemented to learn domain-invariant knowledge and obtain discriminant representations. Predictions of multiple task classifiers that preserve domain structures are optimally merged based on selected similarities for final diagnostic decisions. Extensive cross-domain fault diagnostic experiments validated the effectiveness of the proposed method.

Class-Imbalance Adversarial Transfer Learning Network for Cross-Domain Fault Diagnosis With Imbalanced Data

Recently, cross-domain fault diagnosis based on transfer learning methods has been extensively explored and well-addressed when class-balance data with supervision information are available. However, data under machine faulty states are mostly difficult to collect; there is a huge divide between current transfer learning methods based on implicit class-balance data and real industrial applications. In this article, we propose a class-imbalance adversarial transfer learning (CIATL) network with input being imbalanced data to learn domain-invariant and knowledge. Within this framework, class-imbalance learning is embedded into the adversarial training process to learn class-separate diagnostic knowledge with imbalanced data, double-level adversarial transfer learning including marginal and conditional distribution adaptations is conducted to learn domain-invariant knowledge. Extensive experiments on a planetary gearbox rig with imbalanced data verify the effectiveness and generalization of the proposed method and show its superior performance over contrastive transfer learning methods. Moreover, the proposed method relaxes the underlying assumption that the success of current transfer learning regimes is rooted in class-balance data and extends the application of the transfer learning method for real-industrial cross-domain diagnosis tasks.