Traditional Deep Learning Methods Research Articles

An intensity-based self-supervised domain adaptation method for intervertebral disc segmentation in magnetic resonance imaging.

Accurate IVD segmentation is crucial for diagnosing and treating spinal conditions. Traditional deep learning methods depend on extensive, annotated datasets, which are hard to acquire. This research proposes an intensity-based self-supervised domain adaptation, using unlabeled multi-domain data to reduce reliance on large annotated datasets. The study introduces an innovative method using intensity-based self-supervised learning for IVD segmentation in MRI scans. This approach is particularly suited for IVD segmentations due to its ability to effectively capture the subtle intensity variations that are characteristic of spinal structures. The model, a dual-task system, simultaneously segments IVDs and predicts intensity transformations. This intensity-focused method has the advantages of being easy to train and computationally light, making it highly practical in diverse clinical settings. Trained on unlabeled data from multiple domains, the model learns domain-invariant features, adeptly handling intensity variations across different MRI devices and protocols. Testing on three public datasets showed that this model outperforms baseline models trained on single-domain data. It handles domain shifts and achieves higher accuracy in IVD segmentation. This study demonstrates the potential of intensity-based self-supervised domain adaptation for IVD segmentation. It suggests new directions for research in enhancing generalizability across datasets with domain shifts, which can be applied to other medical imaging fields.

An intensity-based self-supervised domain adaptation method for intervertebral disc segmentation in magnetic resonance imaging.

Accurate IVD segmentation is crucial for diagnosing and treating spinal conditions. Traditional deep learning methods depend on extensive, annotated datasets, which are hard to acquire. This research proposes an intensity-based self-supervised domain adaptation, using unlabeled multi-domain data to reduce reliance on large annotated datasets. The study introduces an innovative method using intensity-based self-supervised learning for IVD segmentation in MRI scans. This approach is particularly suited for IVD segmentations due to its ability to effectively capture the subtle intensity variations that are characteristic of spinal structures. The model, a dual-task system, simultaneously segments IVDs and predicts intensity transformations. This intensity-focused method has the advantages of being easy to train and computationally light, making it highly practical in diverse clinical settings. Trained on unlabeled data from multiple domains, the model learns domain-invariant features, adeptly handling intensity variations across different MRI devices and protocols. Testing on three public datasets showed that this model outperforms baseline models trained on single-domain data. It handles domain shifts and achieves higher accuracy in IVD segmentation. This study demonstrates the potential of intensity-based self-supervised domain adaptation for IVD segmentation. It suggests new directions for research in enhancing generalizability across datasets with domain shifts, which can be applied to other medical imaging fields.

Multi-task deep learning strategy for map-type classification

ABSTRACT The information contained in a map is always represented by text, symbols, and map-type. Among them, map-type is a critical element that denotes the category and theme of map content, which can support map content extraction, map retrieval, and other map data mining tasks. However, the representations of map-type are always so complex and diverse that relies on multiple descriptive labels. Traditional deep learning methods regarding map-type classification are developed by single label, which only supports single-task classification. This means these approaches might overlook the common features among multiple map-type. In this paper, we propose a framework of multi-task deep learning strategy for employing the state-of-the-art deep convolutional neural network models, including ResNet50, MobileNetV2, and Inception-v3, to conduct efficient multi-label map-type classification. Specifically, we develop the dedicated classification module and label selection layer, and integrate them into the backbone of the deep convolutional network model. The experiments revealed that our proposed multi-task classification strategy can achieve greater accuracy in map-type classification, with less processing time required compared to state-of-the-art deep learning regarding map-type classification. This proves that multi-task classification strategy could be competitive to recognize and discover the complex map-type information.

Deep learning with local spatiotemporal structure preserving for soft sensor development of complex industrial processes

Data-driven soft sensors have emerged as indispensable tools for predicting quality variables in complex industrial processes because of their cost-effectiveness and ease of maintenance. In particular, soft sensors based on deep learning have been utilized in extensive research and successful applications in recent years. However, traditional deep learning methods capture hierarchical data features by minimizing global fitting errors, neglecting the local structural characteristics implied in the original data. In this paper, we propose a new deep learning method for soft sensor development. Utilizing autoencoders as the foundational architecture of our network, a new semisupervised strategy is adopted for layerwise pretraining optimization. On the one hand, more representative data features are extracted by maintaining the local spatiotemporal structure of the data; on the other hand, layer-by-layer supervised learning is employed to identify the critical features that are aligned with the ultimate task, which aids in obtaining the optimal network parameters and improving the resulting prediction accuracy. Subsequently, a local spatiotemporal structure-preserving stacked semisupervised autoencoder (LSP-SuAE) is established. To evaluate the feasibility and effectiveness of the proposed approach, experiments are carried out in a real industrial process. A soft sensor based on the LSP-SuAE is developed to predict the rate of ethylbenzene conversion during the dehydrogenation of styrene. The experimental results demonstrate that, compared to five other common or similar data-driven modeling methods, LSP-SuAE exhibits higher prediction accuracy and better stability.

DACLnet: A Dual-Attention-Mechanism CNN-LSTM Network for the Accurate Prediction of Nonlinear InSAR Deformation

Nonlinear deformation is a dynamically changing pattern of multiple surface deformations caused by groundwater overexploitation, underground coal mining, landslides, urban construction, etc., which are often accompanied by severe damage to surface structures or lead to major geological disasters; therefore, the high-precision monitoring and prediction of nonlinear surface deformation is significant. Traditional deep learning methods encounter challenges such as long-term dependencies or difficulty capturing complex spatiotemporal patterns when predicting nonlinear deformations. In this study, we developed a dual-attention-mechanism CNN-LSTM network model (DACLnet) to monitor and accurately predict nonlinear surface deformations precisely. Using advanced time series InSAR results as input, the DACLnet integrates the spatial feature extraction capability of a convolutional neural network (CNN), the advantages of the time series learning of a long short-term memory (LSTM) network, and the enhanced focusing effect of the dual-attention mechanism on crucial information, significantly improving the prediction accuracy of nonlinear surface deformations. The groundwater overexploitation area of the Turpan Basin, China, is selected to test the nonlinear deformation prediction effect of the proposed DACLnet. The results demonstrate that the DACLnet accurately captures developmental trends in historical surface deformations and effectively predicts surface deformations for the next two months in the study area. Compared to traditional LSTM and CNN-LSTM methods, the root mean square error (RMSE) of the DACLnet improved by 85.09% and 68.57%, respectively. These research results can provide crucial technical support for the early warning and prevention of geological disasters and can serve as an effective alternative tool for short-term ground subsidence prediction in areas lacking hydrogeological and other related data.

Memristor-based Bayesian spiking neural network for IBD diagnosis

Traditional deep learning methods, such as deep neural networks, have achieved remarkable success in image processing, natural language processing, graph computation, and other fields. However, in the diagnosis of Inflammatory Bowel Disease (IBD), conventional deep learning methods are highly susceptible to generating overconfident results, which can lead to great loss of life and property if it results in misdiagnosis of the patient. In this work, we propose a memristor-based IBD diagnostic system inspired by the idea of variational inference based on Bayes' law. The memristors in our system are used for both synaptic weight storage and entropy sources so that an efficient Bayesian inference system can be realized. Our memristor-based true random number generator can produce probability-tunable bitstreams required for Bayesian inference with a mean-square error that is reduced by 10.9 × than that of a conventional pseudo-random generator. The proposed system consists of a coarse-grained and a fined-grained spiking neural network, and an optimized strategy that trims the neuron's threshold is proposed for maintaining the appropriate model sparsity and reducing information loss. With Bayesian inference, our work achieves lower diagnostic error rates, which are reduced by 10.79 % and 0.49 % for the coarse-grained and fine-grained networks, respectively. The proposed system is easy to hardware implement and presents a novel solution for community-wide IBD diagnosis.

MiniSeg: An Extremely Minimum Network Based on Lightweight Multiscale Learning for Efficient COVID-19 Segmentation.

The rapid spread of the new pandemic, i.e., coronavirus disease 2019 (COVID-19), has severely threatened global health. Deep-learning-based computer-aided screening, e.g., COVID-19 infected area segmentation from computed tomography (CT) image, has attracted much attention by serving as an adjunct to increase the accuracy of COVID-19 screening and clinical diagnosis. Although lesion segmentation is a hot topic, traditional deep learning methods are usually data-hungry with millions of parameters, easy to overfit under limited available COVID-19 training data. On the other hand, fast training/testing and low computational cost are also necessary for quick deployment and development of COVID-19 screening systems, but traditional methods are usually computationally intensive. To address the above two problems, we propose MiniSeg, a lightweight model for efficient COVID-19 segmentation from CT images. Our efforts start with the design of an attentive hierarchical spatial pyramid (AHSP) module for lightweight, efficient, effective multiscale learning that is essential for image segmentation. Then, we build a two-path (TP) encoder for deep feature extraction, where one path uses AHSP modules for learning multiscale contextual features and the other is a shallow convolutional path for capturing fine details. The two paths interact with each other for learning effective representations. Based on the extracted features, a simple decoder is added for COVID-19 segmentation. For comparing MiniSeg to previous methods, we build a comprehensive COVID-19 segmentation benchmark. Extensive experiments demonstrate that the proposed MiniSeg achieves better accuracy because its only 83k parameters make it less prone to overfitting. Its high efficiency also makes it easy to deploy and develop. The code has been released at https://github.com/yun-liu/MiniSeg.

Res2Net-based multi-scale and multi-attention model for traffic scene image classification.

With the increasing applications of traffic scene image classification in intelligent transportation systems, there is a growing demand for improved accuracy and robustness in this classification task. However, due to weather conditions, time, lighting variations, and annotation costs, traditional deep learning methods still have limitations in extracting complex traffic scene features and achieving higher recognition accuracy. The previous classification methods for traffic scene images had gaps in multi-scale feature extraction and the combination of frequency domain, spatial, and channel attention. To address these issues, this paper proposes a multi-scale and multi-attention model based on Res2Net. Our proposed framework introduces an Adaptive Feature Refinement Pyramid Module (AFRPM) to enhance multi-scale feature extraction, thus improving the accuracy of traffic scene image classification. Additionally, we integrate frequency domain and spatial-channel attention mechanisms to develop recognition capabilities for complex backgrounds, objects of different scales, and local details in traffic scene images. The paper conducts the task of classifying traffic scene images using the Traffic-Net dataset. The experimental results demonstrate that our model achieves an accuracy of 96.88% on this dataset, which is an improvement of approximately 2% compared to the baseline Res2Net network. Furthermore, we validate the effectiveness of the proposed modules through ablation experiments.

Inverse optical scatterometry using sketch-guided deep learning

Optical scatterometry, also referred to as optical critical dimension (OCD) metrology, is a widely used technique for characterizing nanostructures in semiconductor industry. As a model-based optical metrology, the measurement in optical scatterometry is not straightforward but involves solving a complicated inverse problem. So far, the methods for solving the inverse scattering problem, whether traditional or deep-learning-based, necessitate a predefined geometric model, but they are also constrained by this model with poor applicability. Here, we demonstrate a sketch-guided neural network (SGNN) for nanostructure reconstruction in optical scatterometry. By learning from training data based on the designed generic profile model, the neural network acquires not only scattering knowledge but also sketching techniques, that allows it to draw the profiles corresponding to the input optical signature, regardless of whether the sample structure is the same as the generic profile model or not. The accuracy and strong generalizability of proposed approach is validated by using a series of one-dimensional gratings. Experiments have also demonstrated that it is comparable to nonlinear regression methods and outperforms traditional deep learning methods. To our best knowledge, this is the first time that the concept of sketching has been introduced into deep learning for solving the inverse scattering problem. We believe that our method will provide a novel solution for semiconductor metrology, enabling fast and accurate reconstruction of nanostructures.

Wide and Deep Learning Model for Satellite-Based Real-Time Aerosol Retrievals in China

Machine learning methods have been recognized as rapid methods for satellite-based aerosol retrievals but have not been widely applied in geostationary satellites. In this study, we developed a wide and deep learning model to retrieve the aerosol optical depth (AOD) using Himawari-8. Compared to traditional deep learning methods, we embedded a “wide” modeling component and tested the proposed model across China using independent training (2016–2018) and test (2019) datasets. The results showed that the “wide” model improves the accuracy and enhances model interpretability. The estimates exhibited better accuracy (R2 = 0.81, root-mean-square errors (RMSEs) = 0.19, and within the estimated error (EE) = 63%) than those of the deep-only models (R2 = 0.78, RMSE = 0.21, within the EE = 58%). In comparison with extreme gradient boosting (XGBoost) and Himawari-8 V2.1 AOD products, there were also significant improvements. In addition to higher accuracy, the interpretability of the proposed model was superior to that of the deep-only model. Compared with other seasons, higher contributions of spring to the AOD concentrations were interpreted. Based on the application of the wide and deep learning model, the near-real-time variation of the AOD over China could be captured with an ultrafine temporal resolution.

A discrepancy-aware self-distillation method for multi-modal glioma grading

Gliomas are the most common intracranial primary tumors. Accurate grading of gliomas is crucial in determining treatment options and prognosis. Clinicians conventionally rely on multiple Magnetic Resonance Imaging (MRI) sequences for accurate glioma assessment. Traditional deep learning methods typically utilize the individual MRI sequence with the annotations of Region of Interest (ROIs), incurring substantial manual effort while losing the complementary information provided by other sequences. This task is inherently a standard multi-modal problem. However, existing methods often adopt complex multi-stream networks for feature extraction and fusion, leading to high resource demands and potential feature redundancy. To address the above challenge, a discrepancy-aware self-distillation method is proposed for multi-modal glioma grading, which requires only a single-stream network to concurrently analyze multiple MRI sequences without auxiliary ROIs. The first Modality Discrepancy-aware Fusion (MDF) module considers the MRI imaging differentiation and widens the inter-modal contrasts, allowing the modality-specific features to be highlighted and the modality-invariant features to be diminished. Furthermore, the proposed Class Activation Self-Distillation (CASD) strategy leverages the generated Class Activation Maps (CAMs) as dark knowledge for the distillation process. This guides shallow layers to focus on the category discriminative features specifically within the lesion region. Extensive experiments were conducted on the BraTS2018 and BraTS2019 datasets to evaluate the effectiveness of our method. The results show that our method outperforms other relevant glioma grading and multi-modal fusion methods on both datasets.

GTADC: A Graph-Based Method for Inferring Cell Spatial Distribution in Cancer Tissues.

The heterogeneity of tumors poses a challenge for understanding cell interactions and constructing complex ecosystems within cancer tissues. Current research strategies integrate spatial transcriptomics (ST) and single-cell sequencing (scRNA-seq) data to thoroughly analyze this intricate system. However, traditional deep learning methods using scRNA-seq data tend to filter differentially expressed genes through statistical methods. In the context of cancer tissues, where cancer cells exhibit significant differences in gene expression compared to normal cells, this heterogeneity renders traditional analysis methods incapable of accurately capturing differences between cell types. Therefore, we propose a graph-based deep learning method, GTADC, which utilizes Silhouette scores to precisely capture genes with significant expression differences within each cell type, enhancing the accuracy of gene selection. Compared to traditional methods, GTADC not only considers the expression similarity of genes within their respective clusters but also comprehensively leverages information from the overall clustering structure. The introduction of graph structure effectively captures spatial relationships and topological structures between the two types of data, enabling GTADC to more accurately and comprehensively resolve the spatial composition of different cell types within tissues. This refinement allows GTADC to intricately reconstruct the cellular spatial composition, offering a precise solution for inferring cell spatial composition. This method allows for early detection of potential cancer cell regions within tissues, assessing their quantity and spatial information in cell populations. We aim to achieve a preliminary estimation of cancer occurrence and development, contributing to a deeper understanding of early-stage cancer and providing potential support for early cancer diagnosis.

DIML: Deep Interpretable Metric Learning via Structural Matching.

In this paper, we present a new framework named DIML to achieve more interpretable deep metric learning. Unlike traditional deep metric learning method that simply produces a global similarity given two images, DIML computes the overall similarity through the weighted sum of multiple local part-wise similarities, making it easier for human to understand the mechanism of how the model distinguish two images. Specifically, we propose a structural matching strategy that explicitly aligns the spatial embeddings by computing an optimal matching flow between feature maps of the two images. We also devise a multi-scale matching strategy, which considers both global and local similarities and can significantly reduce the computational costs in the application of image retrieval. To handle the view variance in some complicated scenarios, we propose to use cross-correlation as the marginal distribution of the optimal transport to leverage semantic information to locate the important region in the images. Our framework is model-agnostic, which can be applied to off-the-shelf backbone networks and metric learning methods. To extend our DIML to more advanced architectures like vision Transformers (ViTs), we further propose truncated attention rollout and partial similarity to overcome the lack of locality in ViTs. We evaluate our method on three major benchmarks of deep metric learning including CUB200-2011, Cars196, and Stanford Online Products, and achieve substantial improvements over popular metric learning methods with better interpretability.

FedDUS: Lung tumor segmentation on CT images through federated semi-supervised with dynamic update strategy

Background and ObjectiveLung tumor annotation is a key upstream task for further diagnosis and prognosis. Although deep learning techniques have promoted automation of lung tumor segmentation, there remain challenges impeding its application in clinical practice, such as a lack of prior annotation for model training and data-sharing among centers. MethodsIn this paper, we use data from six centers to design a novel federated semi-supervised learning (FSSL) framework with dynamic model aggregation and improve segmentation performance for lung tumors. To be specific, we propose a dynamically updated algorithm to deal with model parameter aggregation in FSSL, which takes advantage of both the quality and quantity of client data. Moreover, to increase the accessibility of data in the federated learning (FL) network, we explore the FAIR data principle while the previous federated methods never involve. ResultThe experimental results show that the segmentation performance of our model in six centers is 0.9348, 0.8436, 0.8328, 0.7776, 0.8870 and 0.8460 respectively, which is superior to traditional deep learning methods and recent federated semi-supervised learning methods. ConclusionThe experimental results demonstrate that our method is superior to the existing FSSL methods. In addition, our proposed dynamic update strategy effectively utilizes the quality and quantity information of client data and shows efficiency in lung tumor segmentation. The source code is released on (https://github.com/GDPHMediaLab/FedDUS).

Multiscale attention for few‐shot image classification

AbstractIn recent years, the application of traditional deep learning methods in the agricultural field using remote sensing techniques, such as crop area and growth monitoring, crop classification, and agricultural disaster monitoring, has been greatly facilitated by advancements in deep learning. The accuracy of image classification plays a crucial role in these applications. Although traditional deep learning methods have achieved significant success in remote sensing image classification, they often involve convolutional neural networks with a large number of parameters that require extensive optimization using numerous remote sensing images for training purposes. To address these challenges, we propose a novel approach called multiscale attention network (MAN) for sample‐based remote sensing image classification. This method consists primarily of feature extractors and attention modules to effectively utilize different scale features through multiscale feature training during the training phase. We evaluate our proposed method on three datasets comprising agricultural remote sensing images and observe superior performance compared to existing approaches. Furthermore, we validate its generalizability by testing it on an oil well indicator diagram specifically designed for classification tasks.

AN EEG-BASED EMOTION RECOGNITION MODEL USING AN INTERACTION DESIGN FRAMEWORK AND DEEP LEARNING

This research makes significant advances in the field of emotion recognition by presenting a new generative adversarial network (GAN) model that integrates deep learning with electroencephalography (EEG). To achieve more accurate data production and real data matching, the model utilizes self-attention and residual neural networks. Additionally, this process is accomplished by substituting an autoencoder for the discriminator in the GAN, and incorporating a reconstruction loss function. We include the self-attention mechanism and residual block in the building of the model to overcome the vanishing gradient problem. This allows the model to acquire information related to emotions in a more in-depth manner, which ultimately results in an improvement in the emotion detection accuracy. The DEAP and MAHNOB-HCI datasets are chosen for the experimental validation portion of this research. These datasets are subsequently compared and analyzed with traditional deep learning methods and well-known emotion identification algorithms. Based on these findings, it is evident that the model that we propose performs exceptionally well on the emotion recognition test, which offers substantial support for studies and applications in this field. In addition, within the context of emotion detection systems, this study places particular emphasis on the crucial role that interaction design frameworks play in enhancing both the user experience and the usability of the system. By pushing the emotion recognition technology boundaries, a new paradigm for the application of deep learning in EEG emotion recognition is provided with this comprehensive research contribution.

Comparison-Transfer Learning Based State-of-Health Estimation for Lithium-Ion Battery

Abstract Rapid and accurate estimation of the state of health of lithium-ion batteries is of great significance. This paper aims to address two issues faced when applying deep learning methods to estimate the health status of lithium-ion batteries: high data quality requirements and poor model generalizability. This paper proposes a comparison-transfer learning approach with cyclic synchronization to estimate the state of health of lithium-ion batteries. First, a cyclic synchronization method based on the Bezier curve fitting algorithm is introduced to synchronize the data obtained at different charge–discharge cycles of the lithium-ion battery, facilitating input to the model. Second, a comparison-transfer network using the Pearson correlation coefficient is proposed to transfer knowledge from the source dataset to predict the target dataset under different environmental temperatures. By training a pre-trained model on the source dataset and utilizing the correlation coefficient to analyze the similarity between the source and target datasets, the accumulated knowledge in the source dataset can be effectively utilized to enhance prediction performance on the target dataset. In the experiments, the proposed method is validated using the lithium-ion battery aging public datasets. The experimental results demonstrate that the proposed approach achieves superior prediction performance in the case of small-sample sizes, exhibiting higher accuracy and stability compared to traditional deep learning methods.

GCN–Informer: A Novel Framework for Mid-Term Photovoltaic Power Forecasting

Predicting photovoltaic (PV) power generation is a crucial task in the field of clean energy. Achieving high-accuracy PV power prediction requires addressing two challenges in current deep learning methods: (1) In photovoltaic power generation prediction, traditional deep learning methods often generate predictions for long sequences one by one, significantly impacting the efficiency of model predictions. As the scale of photovoltaic power stations expands and the demand for predictions increases, this sequential prediction approach may lead to slow prediction speeds, making it difficult to meet real-time prediction requirements. (2) Feature extraction is a crucial step in photovoltaic power generation prediction. However, traditional feature extraction methods often focus solely on surface features, and fail to capture the inherent relationships between various influencing factors in photovoltaic power generation data, such as light intensity, temperature, and more. To overcome these limitations, this paper proposes a mid-term PV power prediction model that combines Graph Convolutional Network (GCN) and Informer models. This fusion model leverages the multi-output capability of the Informer model to ensure the timely generation of predictions for long sequences. Additionally, it harnesses the feature extraction ability of the GCN model from nodes, utilizing graph convolutional modules to extract feature information from the ‘query’ and ‘key’ components within the attention mechanism. This approach provides more reliable feature information for mid-term PV power prediction, thereby ensuring the accuracy of long sequence predictions. Results demonstrate that the GCN–Informer model significantly reduces prediction errors while improving the precision of power generation forecasting compared to the original Informer model. Overall, this research enhances the prediction accuracy of PV power generation and contributes to advancing the field of clean energy.

A novel deep-learning model based on τ-shaped convolutional network (τNet) with long short-term memory (LSTM) for physiological fatigue detection from EEG and EOG signals.

In recent years, fatigue driving has become the main cause of traffic accidents, leading to increased attention towards fatigue detection systems. However, the pooling and strided convolutional operations in fatigue detection algorithm based on traditional deep learning methods may led to the loss of some useful information. This paper proposed a novel -shaped convolutional network ( ) aiming to address this issue. Unlike traditional network structures, incorporates the operations of upsampling features and concatenating high- and low-level features, enabling full utilization of useful information. Moreover, considering that the fatigue state is a mental state involving temporal evolution, we proposed the novel long short-term memory (LSTM)- -shaped convolutional network (LSTM- ), a parallel structure composed of LSTM and for fatigue detection, where extracts time-invariant features with location information, and LSTM extracts long temporal dependencies. We compared LSTM- with six competing methods based on two datasets. Results showed that the proposed algorithm achieved higher classification accuracy than the other methods, with 94.25% on EEG data (binary classification) and 82.19% on EOG data (triple classification). Additionally, the proposed algorithm exhibits low computational cost, good training stability, and robustness against insufficient training. Therefore, it is promising for further implementation of fatigue online detection systems.

Convolutional neural network intelligent diagnosis method using small samples based on SK-CAM

In order to solve the dependence of convolutional neural networks (CNN) on large samples of training data, an intelligent fault diagnosis method based on spectral kurtosis (SK) and attention mechanism is proposed. Firstly, the SK algorithm is used to obtain two-dimensional fast kurtosis graphs from vibration signals, and the two-dimensional fast spectral kurtosis graphs are converted into one-dimensional kurtosis time-domain samples, which are used as the input of CNN. Then the channel attention module (CAM) is added to CNN, and the weight is increased in the channel domain to eliminate the interference of invalid features. The accuracy of fault identification can reach 99.8 % by applying the proposed method on the fault diagnosis experiment of rolling bearings. Compared with the traditional deep learning (DL) method, the proposed method not only has higher accuracy, but also has lower dependence on the number of samples.