EAC-GAN: Semi-supervised Image Enhancement Technology to Improve CNN Classification Performance

TRA-ACGAN: A motor bearing fault diagnosis model based on an auxiliary classifier generative adversarial network and transformer network

To address the challenges of traffic diversity and data imbalance in network intrusion detection, we propose GraphACGAN, a novel detection framework that integrates auxiliary classifier generative adversarial networks (ACGANs) with graph neural networks (GNNs). In this architecture, the GNN is embedded in the ACGAN discriminator to exploit the latent graph structures inherent to network traffic, thereby improving the model's capacity to distinguish between benign and malicious behaviors. Simultaneously, the ACGAN generator was leveraged to synthesize minority‐class attack traffic, effectively mitigating class imbalances and enhancing generalization. Comprehensive experiments conducted on three benchmark datasets, namely, NF‐BoT‐IoT‐v2, NF‐ToN‐IoT‐v2, and NF‐UNSW‐NB15‐v2, demonstrate that GraphACGAN consistently outperforms all baselines, including E‐GraphSAGE, GCN, ACGAN, LSTM, and KNN in terms of accuracy, precision, recall, and F 1 ‐score. An evaluation of resource‐limited computing platforms further demonstrates GraphACGAN's inference efficiency for real‐world deployment.

Convolutional neural networks (CNNs) have been widely used for gas‐insulated switchgear (GIS) partial discharge (PD) pattern recognition due to their powerful feature extraction ability. However, there is commonly a scarcity of fault samples due to low insulation failure rate of GIS equipment, which degrades the diagnostic performance of these CNN networks when directly applied to small and unbalanced datasets. Therefore, we propose a novel auxiliary classifier generative adversarial network for GIS PD pattern recognition for small and unbalanced samples. First, we propose using synchrosqueezed wavelet transform to extract time‐frequency characteristics of PD pulses and obtain a time‐frequency image with high energy aggregation and time‐frequency distribution rate. Then, we propose an improved generative adversarial network with an auxiliary classier and self‐attention mechanism, which can generate high‐quality PD samples for situations with few classes. Experiments show that our proposed method can reach 95.75% recognition accuracy for small datasets, which is the highest among several comparable methods. Furthermore, the proposed method has excellent and stable recognition performance for various unbalanced datasets.

Deep-learning-based SAR ship classification has become a research hotspot in the military and civilian fields and achieved remarkable performance. However, the volume of available SAR ship classification data is relatively small, meaning that previous deep-learning-based methods have usually struggled with overfitting problems. Moreover, due to the limitation of the SAR imaging mechanism, the large intraclass diversity and small interclass similarity further degrade the classification performance. To address these issues, we propose a label smoothing auxiliary classifier generative adversarial network with triplet loss (LST-ACGAN) for SAR ship classification. In our method, an ACGAN is introduced to generate SAR ship samples with category labels. To address the model collapse problem in the ACGAN, the smooth category labels are assigned to generated samples. Moreover, triplet loss is integrated into the ACGAN for discriminative feature learning to enhance the margin of different classes. Extensive experiments on the OpenSARShip dataset demonstrate the superior performance of our method compared to the previous methods.

Effective response strategies to earthquake disasters are crucial for disaster management in smart cities. However, in regions where earthquakes do not occur frequently, model construction may be difficult due to a lack of training data. To address this issue, there is a need for technology that can generate earthquake scenarios for response training at any location. We proposed a model for generating earthquake scenarios using an auxiliary classifier Generative Adversarial Network (AC-GAN)-based data synthesis. The proposed ACGAN model generates various earthquake scenarios by incorporating an auxiliary classifier learning process into the discriminator of GAN. Our results at borehole sensors showed that the seismic data generated by the proposed model had similar characteristics to actual data. To further validate our results, we compared the generated IM (such as PGA, PGV, and SA) with Ground Motion Prediction Equations (GMPE). Furthermore, we evaluated the potential of using the generated scenarios for earthquake early warning training. The proposed model and algorithm have significant potential in advancing seismic analysis and detection management systems, and also contribute to disaster management.

B-mode ultrasound imaging is a popular medical imaging technique. Like other image processing tasks, deep learning has been used for analysis of B-mode ultrasound images in the last few years. However, training deep learning models require large labeled datasets, which is often unavailable for ultrasound images. The lack of large labeled data is a bottleneck for the use of deep learning in ultrasound image analysis. To overcome this challenge, in this work, we exploit auxiliary classifier generative adversarial network (ACGAN) that combines the benefits of data augmentation and transfer learning in the same framework. We conduct experiment on a dataset of breast ultrasound images that shows the effectiveness of the proposed approach.

Improving state-of-health estimation for lithium-ion batteries via unlabeled charging data

Covert timing channels (CTCs) are defined as a mechanism that embeds covert information into network traffic. In a manner, information leakage caused by CTCs brings serious threat to network security. In recent years, detection of CTCs is a focus and a challenging task in the field of covert channel research. However, existing detection schemes based on statistical methods have poor performance in detecting multiple CTCs, and require so many inter-arrival times of packets that these schemes cannot detect CTCs in real time. In this paper, we propose a novel deep learning approach for CTCs detection, namely, covert timing channels detection based on auxiliary classifier generative adversarial network (CD-ACGAN). The network structure and loss function of CD-ACGAN are designed to be suitable for CTCs detection task. We first encode traffic flows into single-channel Gramian Angular Field (GAF) images. Then we use CD-ACGAN to learn features from GAF images and predict the classes of CTCs. Our experimental results show that our approach has high accuracy and strong robustness in detecting various CTCs.

With the growing popularity of smartphones, continuous and implicit authentication of such devices via behavioral biometrics such as touch dynamics becomes an attractive option, especially when the physical biometrics are challenging to utilize, or their frequent and continuous usage annoys the user. However, touch dynamics is vulnerable to potential security attacks such as shoulder surfing, camera attack, and smudge attack. As a result, it is challenging to rule out genuine imposters while only relying on models that learn from real touchstrokes. In this paper, a touchstroke authentication model based on Auxiliary Classifier Generative Adversarial Network (AC-GAN) is presented. Given a small subset of a legitimate user's touchstrokes data during training, the presented AC-GAN model learns to generate a vast amount of synthetic touchstrokes that closely approximate the real touchstrokes, simulating imposter behavior, and then uses both generated and real touchstrokes in discriminating real user from the imposters. The presented network is trained on the Touchanalytics dataset and the discriminability is evaluated with popular performance metrics and loss functions. The evaluation results suggest that it is possible to achieve comparable authentication accuracies with Equal Error Rate ranging from 2% to 11% even when the generative model is challenged with a vast number of synthetic data that effectively simulates an imposter behavior. The use of AC-GAN also diversifies generated samples and stabilizes training.

Accurate classification of brain tumors is often limited by the scarcity of labeled medical images, particularly for complex tumor subtypes. This study proposes an auxiliary classifier generative adversarial network (ACGAN) to generate class-conditioned synthetic MRI images and to enhance the performance of tumor-classification models. The ACGAN was trained using the publicly available Figshare Brain MRI dataset, which contains four categories: glioma, meningioma, pituitary tumor, and healthy tissue. After training, the model produced high-fidelity synthetic MRI images that were quantitatively evaluated using the Fréchet inception distance (FID) as a distribution-level proxy metric, achieving a score of 4.6 and indicating close alignment between the real and synthetic data distributions. In addition to generative quality, the classification network trained with ACGAN-augmented data achieved 94.8% accuracy, 95.3% sensitivity, 93.6% specificity, and 94.1% F1-score, outperforming models trained solely on real data. Comparative analysis demonstrates that the proposed framework provides superior class separability and lower FID values than commonly used generative methods, including DCGAN and standard cGAN approaches. These findings highlight the potential of ACGAN-based synthetic augmentation to support medical-imaging tasks under limited-data conditions and to improve the reliability of brain-tumor classification systems.

Gathering labeled data is one of the most time-consuming and expensive tasks in supervised machine learning. In practical applications, there are usually quite limited labeled training samples but abundant unlabeled data that is easy to collect. Semi-supervised learning and active learning are two important techniques for learning a discriminative classification model when labeled data is scarce. However, unlabeled data with significant noises and outliers cannot be well exploited and usually worsen the performance of semi-supervised learning and the performance of active learning also needs a powerful initial classifier learned from the quite limited labeled training data. In order to solve the above issues, in this paper we proposed a novel model of semi-supervised dictionary active learning (SSDAL), which aims to integrate semi-supervised learning and active learning to effectively use all the training data. In particular, two criterions based on estimated class possibility are designed to select the unlabeled data with confident class estimation for semi-supervised learning and the informative unlabeled data for active learning, respectively. Extensive experiments are conducted to show the superior performance of our method in classification applications, e.g., handwritten digit recognition, face recognition and large-scale image classification.

Summary Deep learning have led to a certain extent breakthrough in seismic facies analysis(SFA). The greatest challenge in the SFA domain is how to cope with large seismic datasets and the limited amount of annotated samples, especially when employing supervised machine learning algorithms that require labelled data and larger training sample. we develop a supervised seismic facies analysis via an improved Auxiliary Classifier Generative Adversarial Network(ACGAN).To solve the problem of imbalanced seismic facies class distribution, and limited training data with labels, we used an improved ACGAN to generate synthetic seismic data sets, and then train Convolutional Neural Networks(CNN) using theses generated synthetic data. Fault data and seismic facies data test demonstrated that generated seismic data via the improved ACGAN can be used for synthetic data augmentation as for increased the performance of CNN and avoided overfitting compared to original seismic data training set. Therefore, we believe that the proposed method can effectively alleviate the problems of insufficient and imbalanced seismic data in SFA, and also can generalize to other seismic data classification applications.

Semi-supervised learning and continuous learning are fundamental paradigms for human-level intelligence. To deal with real-world problems where labels are rarely given and the opportunity to access the same data is limited, it is necessary to apply these two paradigms in a joined fashion. In this paper, we propose Label Propagation Adaptive Resonance Theory (LPART) for semi-supervised continuous learning. LPART uses an online label propagation mechanism to perform classification and gradually improves its accuracy as the observed data accumulates. We evaluated the proposed model on visual (MNIST, SVHN, CIFAR-10) and audio (NSynth) datasets by adjusting the ratio of the labeled and unlabeled data. The accuracies are much higher when both labeled and unlabeled data are used, demonstrating the significant advantage of LPART in environments where the data labels are scarce.

Traditional Steganography need to modify the carrier image to hide information, which will leave traces of rewriting, then eventually be perceived by the enemy. In this paper, an information hiding scheme based on Auxiliary Classifier Generative Adversarial Networks (AC-GANs) model is proposed for Steganography. This method designs and trains the networks model based on AC-GANs by constructing a dedicated dictionary and image database. The sender can map the secret information into the category labels through the dictionary, and then use the labels generate the real looking images to be sent through the model. On the contrary, the receiver can identify the image label through the model and obtain the secret information. Through experiments, the feasibility of this method is verified and the reliability of the algorithm is analyzed. This method transmits secret messages by generating images without overwriting the carrier images. It can effectively solve the problem of modification of carrier images in traditional information hiding.

Rotating machinery is widely employed in critical industries such as petrochemical, aerospace, and rail transportation, where its operational condition is vital for ensuring system safety and reliability. However, fault diagnosis in such machinery is challenged by the limited availability of data samples in real-world applications. Traditional diagnostic methods often fail to significantly improve fault detection accuracy under these conditions. To address this issue, this paper proposes a fault diagnosis method tailored for rotating machinery with limited sample sizes, combining enhanced auxiliary classifier generative adversarial networks
(EACGAN) with a multi-scale attention convolutional neural network (MSACNN). First, a continuous wavelet transform is applied to convert the one-dimensional vibration signal into a time-frequency spectrogram, generating a time-frequency sample dataset. Next, the model structure and loss function are refined to develop an EACGAN capable of generating high-quality data samples for data augmentation. Finally, a multi-scale convolutional network is introduced, and in conjunction with the dual-pooling efficient channel attention (DP-ECA) mechanism, the MSACNN is designed to enhance diagnostic recognition accuracy.
Experimental results from bearing and gear datasets show that the proposed method can accurately identify multi-modal faults in rotating machinery, even with limited sample sizes, significantly improving diagnostic performance.