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Journal articles on the topic 'Generative classifiers'

Author: Grafiati

Published: 11 March 2023

Last updated: 31 July 2025

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1

Varga, Michal, Ján Jadlovský, and Slávka Jadlovská. "Generative Enhancement of 3D Image Classifiers." Applied Sciences 10, no. 21 (2020): 7433. http://dx.doi.org/10.3390/app10217433.

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In this paper, we propose a methodology for generative enhancement of existing 3D image classifiers. This methodology is based on combining the advantages of both non-generative classifiers and generative modeling. Its purpose is to streamline the synthesis of novel deep neural networks by embedding existing compatible classifiers into a generative network architecture. A demonstration of this process and evaluation of its effectiveness is performed using a 3D convolutional classifier and its generative equivalent—a 3D conditional generative adversarial network classifier. The results of the e

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Zervou, Michaela, Effrosyni Doutsi, Yannis Pantazis, and Panagiotis Tsakalides. "De Novo Antimicrobial Peptide Design with Feedback Generative Adversarial Networks." International Journal of Molecular Sciences 25, no. 10 (2024): 5506. http://dx.doi.org/10.3390/ijms25105506.

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Antimicrobial peptides (AMPs) are promising candidates for new antibiotics due to their broad-spectrum activity against pathogens and reduced susceptibility to resistance development. Deep-learning techniques, such as deep generative models, offer a promising avenue to expedite the discovery and optimization of AMPs. A remarkable example is the Feedback Generative Adversarial Network (FBGAN), a deep generative model that incorporates a classifier during its training phase. Our study aims to explore the impact of enhanced classifiers on the generative capabilities of FBGAN. To this end, we intr

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Shakhuro, V. I., and A. S. Konushin. "IMAGE SYNTHESIS WITH NEURAL NETWORKS FOR TRAFFIC SIGN CLASSIFICATION." Computer Optics 42, no. 1 (2018): 105–12. http://dx.doi.org/10.18287/2412-6179-2018-42-1-105-112.

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In this work, we research the applicability of generative adversarial neural networks for generating training samples for a traffic sign classification task. We consider generative neural networks trained using the Wasserstein metric. As a baseline method for comparison, we take image generation based on traffic sign icons. Experimental evaluation of the classifiers based on convolutional neural networks is conducted on real data, two types of synthetic data, and a combination of real and synthetic data. The experiments show that modern generative neural networks are capable of generating real

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Hassan, Anthony Rotimi, Rasaki Olawale Olanrewaju, Queensley C. Chukwudum, Sodiq Adejare Olanrewaju, and S. E. Fadugba. "Comparison Study of Generative and Discriminative Models for Classification of Classifiers." International Journal of Mathematics and Computers in Simulation 16 (June 28, 2022): 76–87. http://dx.doi.org/10.46300/9102.2022.16.12.

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In classification of classifier analysis, researchers have been worried about the classifier of existing generative and discriminative models in practice for analyzing attributes data. This makes it necessary to give an in-depth, systematic, interrelated, interconnected, and classification of classifier of generative and discriminative models. Generative models of Logistic and Multinomial Logistic regression models and discriminative models of Linear Discriminant Analysis (LDA) (for attribute P=1 and P>1), Quadratic Discriminant Analysis (QDA) and Naïve Bayes were thoroughly dealt with anal

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Joo, Jaehan, Sang Yoon Kim, Donghwan Kim, et al. "Enhancing automated strabismus classification with limited data: Data augmentation using StyleGAN2-ADA." PLOS ONE 19, no. 5 (2024): e0303355. http://dx.doi.org/10.1371/journal.pone.0303355.

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In this study, we propose a generative data augmentation technique to overcome the challenges of severely limited data when designing a deep learning-based automated strabismus diagnosis system. We implement a generative model based on the StyleGAN2-ADA model for system design and assess strabismus classification performance using two classifiers. We evaluate the capability of our proposed method against traditional data augmentation techniques and confirm a substantial enhancement in performance. Furthermore, we conduct experiments to explore the relationship between the diagnosis agreement a

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Anil, Gautham, Vishnu Vinod, and Apurva Narayan. "Generating Universal Adversarial Perturbations for Quantum Classifiers." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 10891–99. http://dx.doi.org/10.1609/aaai.v38i10.28963.

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Quantum Machine Learning (QML) has emerged as a promising field of research, aiming to leverage the capabilities of quantum computing to enhance existing machine learning methodologies. Recent studies have revealed that, like their classical counterparts, QML models based on Parametrized Quantum Circuits (PQCs) are also vulnerable to adversarial attacks. Moreover, the existence of Universal Adversarial Perturbations (UAPs) in the quantum domain has been demonstrated theoretically in the context of quantum classifiers. In this work, we introduce QuGAP: a novel framework for generating UAPs for

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Abady, Lydia, Giovanna Maria Dimitri, and Mauro Barni. "A One-Class Classifier for the Detection of GAN Manipulated Multi-Spectral Satellite Images." Remote Sensing 16, no. 5 (2024): 781. http://dx.doi.org/10.3390/rs16050781.

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The current image generative models have achieved a remarkably realistic image quality, offering numerous academic and industrial applications. However, to ensure these models are used for benign purposes, it is essential to develop tools that definitively detect whether an image has been synthetically generated. Consequently, several detectors with excellent performance in computer vision applications have been developed. However, these detectors cannot be directly applied as they areto multi-spectral satellite images, necessitating the training of new models. While two-class classifiers gene

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Kumar Bhowmik, Tapan. "Naive Bayes vs Logistic Regression: Theory, Implementation and Experimental Validation." Inteligencia Artificial 18, no. 56 (2015): 14. http://dx.doi.org/10.4114/intartif.vol18iss56pp14-30.

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This article presents the theoretical derivation as well as practical steps for implementing Naive Bayes (NB) and Logistic Regression (LR) classifiers. A generative learning under Gaussian Naive Bayes assumption and two discriminative learning techniques based on gradient ascent and Newton-Raphson methods are described to estimate the parameters of LR. Some limitation of learning techniques and implementation issues are discussed as well. A set of experiments are performed for both the classifiers under different learning circumstances and their performances are compared. From the experiments,

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Lu, Zhengdong, Todd K. Leen, and Jeffrey Kaye. "Kernels for Longitudinal Data with Variable Sequence Length and Sampling Intervals." Neural Computation 23, no. 9 (2011): 2390–420. http://dx.doi.org/10.1162/neco_a_00164.

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We develop several kernel methods for classification of longitudinal data and apply them to detect cognitive decline in the elderly. We first develop mixed-effects models, a type of hierarchical empirical Bayes generative models, for the time series. After demonstrating their utility in likelihood ratio classifiers (and the improvement over standard regression models for such classifiers), we develop novel Fisher kernels based on mixture of mixed-effects models and use them in support vector machine classifiers. The hierarchical generative model allows us to handle variations in sequence lengt

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Sensoy, Murat, Lance Kaplan, Federico Cerutti, and Maryam Saleki. "Uncertainty-Aware Deep Classifiers Using Generative Models." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5620–27. http://dx.doi.org/10.1609/aaai.v34i04.6015.

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Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to output high uncertainty for the data samples close to class boundaries or from the outside of the training distribution. These approaches use an auxiliary data set during training to represent out-of-distribution samples. However, selection or creation of such an auxiliary data set is non-trivial, especially for high dimensional data such as images. In this work we develop a nove

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Joshi, Santosh, Alexander Perez Pons, Shrirang Ambaji Kulkarni, and Himanshu Upadhyay. "Application of Machine Learning Models for Malware Classification With Real and Synthetic Datasets." International Journal of Information Security and Privacy 18, no. 1 (2024): 1–23. http://dx.doi.org/10.4018/ijisp.356513.

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Stacking of multiple Machine Learning (ML) classifiers have gained popularity in addressing anomalous data classification along with Deep Learning (DL) algorithms. This study compares traditional ML classifiers, multi-layer stacking ML classifiers, and DL classifiers using an open-source malware dataset-containing equal numbers of benign and malware samples. The results on the realistic dataset indicate that the DL classifier, utilizing a Bidirectional Long Short-Term Memory (BiLSTM) model, outperformed the stacked classifiers with Logistic Regression (LR) and Support Vector Machine (SVM) as M

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Amiryousefi, Ali, Ville Kinnula, and Jing Tang. "Bayes in Wonderland! Predictive Supervised Classification Inference Hits Unpredictability." Mathematics 10, no. 5 (2022): 828. http://dx.doi.org/10.3390/math10050828.

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The marginal Bayesian predictive classifiers (mBpc), as opposed to the simultaneous Bayesian predictive classifiers (sBpc), handle each data separately and, hence, tacitly assume the independence of the observations. Due to saturation in learning of generative model parameters, the adverse effect of this false assumption on the accuracy of mBpc tends to wear out in the face of an increasing amount of training data, guaranteeing the convergence of these two classifiers under the de Finetti type of exchangeability. This result, however, is far from trivial for the sequences generated under Parti

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Hossin, M., and M.N Sulaiman. "A Review on Evaluation Metrics for Data Classification Evaluations." International Journal of Data Mining & Knowledge Management Process (IJDKP) 5, no. 2 (2019): 1–11. https://doi.org/10.5281/zenodo.3557376.

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Evaluation metric plays a critical role in achieving the optimal classifier during the classification training. Thus, a selection of suitable evaluation metric is an important key for discriminating and obtaining the optimal classifier. This paper systematically reviewed the related evaluation metrics that are specifically designed as a discriminator for optimizing generative classifier. Generally, many generative classifiers employ accuracy as a measure to discriminate the optimal solution during the classification training. However, the accuracy has several weaknesses which are less distinct

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Elzobi, Moftah, and Ayoub Al-Hamadi. "Generative vs. Discriminative Recognition Models for Off-Line Arabic Handwriting." Sensors 18, no. 9 (2018): 2786. http://dx.doi.org/10.3390/s18092786.

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The majority of handwritten word recognition strategies are constructed on learning-based generative frameworks from letter or word training samples. Theoretically, constructing recognition models through discriminative learning should be the more effective alternative. The primary goal of this research is to compare the performances of discriminative and generative recognition strategies, which are described by generatively-trained hidden Markov modeling (HMM), discriminatively-trained conditional random fields (CRF) and discriminatively-trained hidden-state CRF (HCRF). With learning samples

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Chang, Allen, Matthew C. Fontaine, Serena Booth, Maja J. Matarić, and Stefanos Nikolaidis. "Quality-Diversity Generative Sampling for Learning with Synthetic Data." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 18 (2024): 19805–12. http://dx.doi.org/10.1609/aaai.v38i18.29955.

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Generative models can serve as surrogates for some real data sources by creating synthetic training datasets, but in doing so they may transfer biases to downstream tasks. We focus on protecting quality and diversity when generating synthetic training datasets. We propose quality-diversity generative sampling (QDGS), a framework for sampling data uniformly across a user-defined measure space, despite the data coming from a biased generator. QDGS is a model-agnostic framework that uses prompt guidance to optimize a quality objective across measures of diversity for synthetically generated data,

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Mercaldo, Francesco, Luca Brunese, Fabio Martinelli, Antonella Santone, and Mario Cesarelli. "Generative Adversarial Networks in Retinal Image Classification." Applied Sciences 13, no. 18 (2023): 10433. http://dx.doi.org/10.3390/app131810433.

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The recent introduction of generative adversarial networks has demonstrated remarkable capabilities in generating images that are nearly indistinguishable from real ones. Consequently, both the academic and industrial communities have raised concerns about the challenge of differentiating between fake and real images. This issue holds significant importance, as images play a vital role in various domains, including image recognition and bioimaging classification in the biomedical field. In this paper, we present a method to assess the distinguishability of bioimages generated by a generative a

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Mercaldo, Francesco, Fabio Martinelli, and Antonella Santone. "Deep Convolutional Generative Adversarial Networks in Image-Based Android Malware Detection." Computers 13, no. 6 (2024): 154. http://dx.doi.org/10.3390/computers13060154.

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The recent advancements in generative adversarial networks have showcased their remarkable ability to create images that are indistinguishable from real ones. This has prompted both the academic and industrial communities to tackle the challenge of distinguishing fake images from genuine ones. We introduce a method to assess whether images generated by generative adversarial networks, using a dataset of real-world Android malware applications, can be distinguished from actual images. Our experiments involved two types of deep convolutional generative adversarial networks, and utilize images de

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Epshteyn, A., and G. DeJong. "Generative Prior Knowledge for Discriminative Classification." Journal of Artificial Intelligence Research 27 (September 25, 2006): 25–53. http://dx.doi.org/10.1613/jair.1934.

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We present a novel framework for integrating prior knowledge into discriminative classifiers. Our framework allows discriminative classifiers such as Support Vector Machines (SVMs) to utilize prior knowledge specified in the generative setting. The dual objective of fitting the data and respecting prior knowledge is formulated as a bilevel program, which is solved (approximately) via iterative application of second-order cone programming. To test our approach, we consider the problem of using WordNet (a semantic database of English language) to improve low-sample classification accuracy of new

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Wang, Tianshi, Li Liu, Huaxiang Zhang, Long Zhang, and Xiuxiu Chen. "Joint Character-Level Convolutional and Generative Adversarial Networks for Text Classification." Complexity 2020 (April 30, 2020): 1–11. http://dx.doi.org/10.1155/2020/8516216.

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With the continuous renewal of text classification rules, text classifiers need more powerful generalization ability to process the datasets with new text categories or small training samples. In this paper, we propose a text classification framework under insufficient training sample conditions. In the framework, we first quantify the texts by a character-level convolutional neural network and input the textual features into an adversarial network and a classifier, respectively. Then, we use the real textual features to train a generator and a discriminator so as to make the distribution of g

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Chen, Wei, Xinmiao Chen, and Xiao Sun. "Emotional dialog generation via multiple classifiers based on a generative adversarial network." Virtual Reality & Intelligent Hardware 3, no. 1 (2021): 18–32. http://dx.doi.org/10.1016/j.vrih.2020.12.001.

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Montero, Alberto, Elisenda Bonet-Carne, and Xavier Paolo Burgos-Artizzu. "Generative Adversarial Networks to Improve Fetal Brain Fine-Grained Plane Classification." Sensors 21, no. 23 (2021): 7975. http://dx.doi.org/10.3390/s21237975.

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Generative adversarial networks (GANs) have been recently applied to medical imaging on different modalities (MRI, CT, X-ray, etc). However there are not many applications on ultrasound modality as a data augmentation technique applied to downstream classification tasks. This study aims to explore and evaluate the generation of synthetic ultrasound fetal brain images via GANs and apply them to improve fetal brain ultrasound plane classification. State of the art GANs stylegan2-ada were applied to fetal brain image generation and GAN-based data augmentation classifiers were compared with baseli

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Du, Xiuli, Xiaohui Ding, Meiling Xi, Yana Lv, Shaoming Qiu, and Qingli Liu. "A Data Augmentation Method for Motor Imagery EEG Signals Based on DCGAN-GP Network." Brain Sciences 14, no. 4 (2024): 375. http://dx.doi.org/10.3390/brainsci14040375.

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Motor imagery electroencephalography (EEG) signals have garnered attention in brain–computer interface (BCI) research due to their potential in promoting motor rehabilitation and control. However, the limited availability of labeled data poses challenges for training robust classifiers. In this study, we propose a novel data augmentation method utilizing an improved Deep Convolutional Generative Adversarial Network with Gradient Penalty (DCGAN-GP) to address this issue. We transformed raw EEG signals into two-dimensional time–frequency maps and employed a DCGAN-GP network to generate synthetic

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D'souza, Annie, Swetha M, and Sunita Sarawagi. "Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap Class." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 16127–34. https://doi.org/10.1609/aaai.v39i15.33771.

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Handling imbalance in class distribution when building a classifier over tabular data has been a problem of long-standing interest. One popular approach is augmenting the training dataset with synthetically generated data. While classical augmentation techniques were limited to linear interpolation of existing minority class examples, recently higher capacity deep generative models are providing greater promise. However, handling of imbalance in class distribution when building a deep generative model is also a challenging problem, that has not been studied as extensively as imbalanced classif

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Sandouka, Soha B., Yakoub Bazi, Haikel Alhichri, and Naif Alajlan. "Unified Generative Adversarial Networks for Multidomain Fingerprint Presentation Attack Detection." Entropy 23, no. 8 (2021): 1089. http://dx.doi.org/10.3390/e23081089.

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With the rapid growth of fingerprint-based biometric systems, it is essential to ensure the security and reliability of the deployed algorithms. Indeed, the security vulnerability of these systems has been widely recognized. Thus, it is critical to enhance the generalization ability of fingerprint presentation attack detection (PAD) cross-sensor and cross-material settings. In this work, we propose a novel solution for addressing the case of a single source domain (sensor) with large labeled real/fake fingerprint images and multiple target domains (sensors) with only few real images obtained f

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Simon, Judy. "Image Augmentation based on GAN deep learning approach with Textual Content Descriptors." September 2021 3, no. 3 (2021): 210–25. http://dx.doi.org/10.36548/jitdw.2021.3.005.

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Computer vision, also known as computational visual perception, is a branch of artificial intelligence that allows computers to interpret digital pictures and videos in a manner comparable to biological vision. It entails the development of techniques for simulating biological vision. The aim of computer vision is to extract more meaningful information from visual input than that of a biological vision. Computer vision is exploding due to the avalanche of data being produced today. Powerful generative models, such as Generative Adversarial Networks (GANs), are responsible for significant advan

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Guida, Ciro. "Toward Privacy-Preserving Training of Generative AI Models for Network Traffic Classification." ACM SIGMETRICS Performance Evaluation Review 52, no. 4 (2025): 6–8. https://doi.org/10.1145/3725536.3725540.

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Synthetic traffic traces are useful for training traffic classifiers in privacy-constrained environments. Generative Artificial Intelligence (GAI) models are blossoming as a solution to avoid the sharing of real data and the lack of datasets. Never the less ,privacy concerns about GAI are often under-estimated. Therefore, an approach to mitigate the data leakage of a GAI is presented in this paper, with a minimum impact on the utility of synthetic traffic traces for downstream applications. For example, training a Machine Learning (ML) traffic classifier on synthetic traffic traces results in

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Karaliutė, Marta, and Kęstutis Dučinskas. "Performance of the supervised generative classifiers of spatio-temporal areal data using various spatial autocorrelation indexes." Nonlinear Analysis: Modelling and Control 28 (February 22, 2023): 1–14. http://dx.doi.org/10.15388/namc.2023.28.31434.

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This article is concerned with a generative approach to supervised classification of spatio-temporal data collected at fixed areal units and modeled by Gaussian Markov random field. We focused on the classifiers based on Bayes discriminant functions formed by the log-ratio of the class conditional likelihoods. As a novel modeling contribution, we propose to use decision threshold values induced by three popular spatial autocorrelation indexes, i.e., Moran’s I, Geary’s C and Getis–Ord G. The goal of this study is to extend the recent investigations in the context of geostatistical and hidden Ma

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Graña, Manuel, Leire Ozaeta, and Darya Chyzhyk. "Resting State Effective Connectivity Allows Auditory Hallucination Discrimination." International Journal of Neural Systems 27, no. 05 (2017): 1750019. http://dx.doi.org/10.1142/s0129065717500198.

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Hallucinations are elusive phenomena that have been associated with psychotic behavior, but that have a high prevalence in healthy population. Some generative mechanisms of Auditory Hallucinations (AH) have been proposed in the literature, but so far empirical evidence is scarce. The most widely accepted generative mechanism hypothesis nowadays consists in the faulty workings of a network of brain areas including the emotional control, the audio and language processing, and the inhibition and self-attribution of the signals in the auditive cortex. In this paper, we consider two methods to anal

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Bhuvaneswari, M. "Gaussian mixture model: An application to parameter estimation and medical image classification." Journal of Scientific and Innovative Research 5, no. 3 (2016): 100–105. http://dx.doi.org/10.31254/jsir.2016.5308.

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Gaussian mixture model based parameter estimation and classification has recently received great attention in modelling and processin g data. Gaussian Mixture Model (GMM) is the probabilistic model for representing the presence of subpopulations and it works well with the classification and parameter estimation strategy. Here in this work Maximum Likelihood Estimation (MLE) based on Expectation Maximization (EM) is being used for the parameter estimation approach and the estimated parameters are being used for the training and the testing of the images for their normality and the abnormality.

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Perina, Alessandro, Marco Cristani, Umberto Castellani, Vittorio Murino, and Nebojsa Jojic. "Free Energy Score Spaces: Using Generative Information in Discriminative Classifiers." IEEE Transactions on Pattern Analysis and Machine Intelligence 34, no. 7 (2012): 1249–62. http://dx.doi.org/10.1109/tpami.2011.241.

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Fisch, Dominik, Edgar Kalkowski, and Bernhard Sick. "Knowledge Fusion for Probabilistic Generative Classifiers with Data Mining Applications." IEEE Transactions on Knowledge and Data Engineering 26, no. 3 (2014): 652–66. http://dx.doi.org/10.1109/tkde.2013.20.

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Zhen, Hao, Yucheng Shi, Jidong J. Yang, and Javad Mohammadpour Vehni. "Co-supervised learning paradigm with conditional generative adversarial networks for sample-efficient classification." Applied Computing and Intelligence 3, no. 1 (2022): 13–26. http://dx.doi.org/10.3934/aci.2023002.

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<abstract> <p>Classification using supervised learning requires annotating a large amount of classes-balanced data for model training and testing. This has practically limited the scope of applications with supervised learning, in particular deep learning. To address the issues associated with limited and imbalanced data, this paper introduces a sample-efficient co-supervised learning paradigm (SEC-CGAN), in which a conditional generative adversarial network (CGAN) is trained alongside the classifier and supplements semantics-conditioned, confidence-aware synthesized examples to th

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Al-Qaderi, Mohammad, Elfituri Lahamer, and Ahmad Rad. "A Two-Level Speaker Identification System via Fusion of Heterogeneous Classifiers and Complementary Feature Cooperation." Sensors 21, no. 15 (2021): 5097. http://dx.doi.org/10.3390/s21155097.

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We present a new architecture to address the challenges of speaker identification that arise in interaction of humans with social robots. Though deep learning systems have led to impressive performance in many speech applications, limited speech data at training stage and short utterances with background noise at test stage present challenges and are still open problems as no optimum solution has been reported to date. The proposed design employs a generative model namely the Gaussian mixture model (GMM) and a discriminative model—support vector machine (SVM) classifiers as well as prosodic fe

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Yakura, Hiromu, Youhei Akimoto, and Jun Sakuma. "Generate (Non-Software) Bugs to Fool Classifiers." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 1070–78. http://dx.doi.org/10.1609/aaai.v34i01.5457.

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In adversarial attacks intended to confound deep learning models, most studies have focused on limiting the magnitude of the modification so that humans do not notice the attack. On the other hand, during an attack against autonomous cars, for example, most drivers would not find it strange if a small insect image were placed on a stop sign, or they may overlook it. In this paper, we present a systematic approach to generate natural adversarial examples against classification models by employing such natural-appearing perturbations that imitate a certain object or signal. We first show the fea

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Nan, Zhixiong, Yang Liu, Nanning Zheng, and Song-Chun Zhu. "Recognizing Unseen Attribute-Object Pair with Generative Model." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8811–18. http://dx.doi.org/10.1609/aaai.v33i01.33018811.

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In this paper, we are studying the problem of recognizing attribute-object pairs that do not appear in the training dataset, which is called unseen attribute-object pair recognition. Existing methods mainly learn a discriminative classifier or compose multiple classifiers to tackle this problem, which exhibit poor performance for unseen pairs. The key reasons for this failure are 1) they have not learned an intrinsic attributeobject representation, and 2) the attribute and object are processed either separately or equally so that the inner relation between the attribute and object has not been

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Liakos, Konstantinos G., Georgios K. Georgakilas, Fotis C. Plessas, and Paris Kitsos. "GAINESIS: Generative Artificial Intelligence NEtlists SynthesIS." Electronics 11, no. 2 (2022): 245. http://dx.doi.org/10.3390/electronics11020245.

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A significant problem in the field of hardware security consists of hardware trojan (HT) viruses. The insertion of HTs into a circuit can be applied for each phase of the circuit chain of production. HTs degrade the infected circuit, destroy it or leak encrypted data. Nowadays, efforts are being made to address HTs through machine learning (ML) techniques, mainly for the gate-level netlist (GLN) phase, but there are some restrictions. Specifically, the number and variety of normal and infected circuits that exist through the free public libraries, such as Trust-HUB, are based on the few sample

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Wang, Guangxing, and Peng Ren. "Hyperspectral Image Classification with Feature-Oriented Adversarial Active Learning." Remote Sensing 12, no. 23 (2020): 3879. http://dx.doi.org/10.3390/rs12233879.

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Deep learning classifiers exhibit remarkable performance for hyperspectral image classification given sufficient labeled samples but show deficiency in the situation of learning with limited labeled samples. Active learning endows deep learning classifiers with the ability to alleviate this deficiency. However, existing active deep learning methods tend to underestimate the feature variability of hyperspectral images when querying informative unlabeled samples subject to certain acquisition heuristics. A major reason for this bias is that the acquisition heuristics are normally derived based o

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Rahman, Md Abdur, Guillermo A. Francia, and Hossain Shahriar. "Leveraging GANs for Synthetic Data Generation to Improve Intrusion Detection Systems." Journal of Future Artificial Intelligence and Technologies 1, no. 4 (2025): 429–39. https://doi.org/10.62411/faith.3048-3719-52.

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This research presents a hybrid intrusion detection approach that integrates Generative Adversarial Networks (GANs) for synthetic data generation with Random Forest (RF) as the primary classifier. The study aims to improve detection performance in cybersecurity applications by enhancing dataset diversity and addressing challenges in traditional models, particularly in detecting minority attack classes often underrepresented in real-world datasets. The proposed method employs GANs to generate synthetic attack samples that mimic real-world intrusions, which are then combined with real data from

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Geng, Lin, Ningzhong Liu, and Jie Qin. "Multi-Classifier Adversarial Optimization for Active Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 7687–95. http://dx.doi.org/10.1609/aaai.v37i6.25932.

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Active learning (AL) aims to find a better trade-off between labeling costs and model performance by consciously selecting more informative samples to label. Recently, adversarial approaches have emerged as effective solutions. Most of them leverage generative adversarial networks to align feature distributions of labeled and unlabeled data, upon which discriminators are trained to better distinguish between them. However, these methods fail to consider the relationship between unlabeled samples and decision boundaries, and their training processes are often complex and unstable. To this end,

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Fisch, Dominik, Christian Gruhl, Edgar Kalkowski, Bernhard Sick, and Seppo J. Ovaska. "Towards automation of knowledge understanding: An approach for probabilistic generative classifiers." Information Sciences 370-371 (November 2016): 476–96. http://dx.doi.org/10.1016/j.ins.2016.08.016.

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Li, Yuanzhang, Yaxiao Wang, Ye Wang, Lishan Ke, and Yu-an Tan. "A feature-vector generative adversarial network for evading PDF malware classifiers." Information Sciences 523 (June 2020): 38–48. http://dx.doi.org/10.1016/j.ins.2020.02.075.

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Ivo, Verhoeven, Chen Xinyi, Hu Qingzhi, and Holubar Mario. "[Re] Replication Study of 'Generative causal explanations of black-box classifiers'." ReScience C 7, no. 2 (2021): #23. https://doi.org/10.5281/zenodo.4835600.

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Avdoshin, S. M., D. V. Pantiukhin, I. M. Voronkov, et al. "Analysis of Neural Network Intrusion Detection Methods and Datasets for their Training." Informacionnye Tehnologii 28, no. 12 (2022): 644–53. http://dx.doi.org/10.17587/it.28.644-653.

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Approaches based on neural network classifiers to the detection of computer attacks are considered. The problems of training such classifiers are discussed. Data sets on computer attacks for wired and wireless systems are considered. The results of evaluating such sets by the degree of imbalance are given. The problems of learning on unbalanced data sets and approaches to balancing the training set in the case of rare attacks, including those using generative adversarial networks, are described.

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Guttà, Cristiano, Christoph Morhard, and Markus Rehm. "Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer." PLOS Computational Biology 19, no. 4 (2023): e1011035. http://dx.doi.org/10.1371/journal.pcbi.1011035.

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Established prognostic tests based on limited numbers of transcripts can identify high-risk breast cancer patients yet are approved only for individuals presenting with specific clinical features or disease characteristics. Deep learning algorithms could hold potential for stratifying patient cohorts based on full transcriptome data, yet the development of robust classifiers is hampered by the number of variables in omics datasets typically far exceeding the number of patients. To overcome this hurdle, we propose a classifier based on a data augmentation pipeline consisting of a Wasserstein ge

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Florida, Mary, Vadhana Kumar S, Deepa A.J., Sworna Kokila M.L., and Brilly Sangeetha S. "DEEP GENERATIVE DISCRETE COSINE TRANSFORM FOR SPECTRAL IMAGE PROCESSING." ICTACT Journal on Image and Video Processing 12, no. 4 (2022): 2746–49. https://doi.org/10.21917/ijivp.2022.0390.

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The ever-increasing number of publications and applications in the field of cross-spectral image processing has led to the area receiving greater focus than it previously had. In cross-spectral frameworks, the data from hyperspectral bands is blended with the data from other spectral bands in order to provide responses that are more robust to particular obstacles. Cross-spectral processing could be useful for a variety of applications, including dehazing, segmentation, calculating the vegetation index, and face identification, to name just a few of them. The availability of cross-and multi-spe

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Abedi, Masoud, Lars Hempel, Sina Sadeghi, and Toralf Kirsten. "GAN-Based Approaches for Generating Structured Data in the Medical Domain." Applied Sciences 12, no. 14 (2022): 7075. http://dx.doi.org/10.3390/app12147075.

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Modern machine and deep learning methods require large datasets to achieve reliable and robust results. This requirement is often difficult to meet in the medical field, due to data sharing limitations imposed by privacy regulations or the presence of a small number of patients (e.g., rare diseases). To address this data scarcity and to improve the situation, novel generative models such as Generative Adversarial Networks (GANs) have been widely used to generate synthetic data that mimic real data by representing features that reflect health-related information without reference to real patien

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Machado, Jorge, Ana Marta, Pedro Mestre, João Melo Beirão, and António Cunha. "Data Augmentation with Generative Methods for Inherited Retinal Diseases: A Systematic Review." Applied Sciences 15, no. 6 (2025): 3084. https://doi.org/10.3390/app15063084.

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Inherited retinal diseases (IRDs) are rare and genetically diverse disorders that cause progressive vision loss and affect 1 in 3000 individuals worldwide. Their rarity and genetic variability pose a challenge for deep learning models due to the limited amount of data. Generative models offer a promising solution by creating synthetic data to improve training datasets. This study carried out a systematic literature review to investigate the use of generative models to augment data in IRDs and assess their impact on the performance of classifiers for these diseases. Following PRISMA 2020 guidel

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Wang, Liwei, Xiong Li, Zhuowen Tu, and Jiaya Jia. "Discriminative Clustering via Generative Feature Mapping." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 1162–68. http://dx.doi.org/10.1609/aaai.v26i1.8305.

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Existing clustering methods can be roughly classified into two categories: generative and discriminative approaches. Generative clustering aims to explain the data and thus is adaptive to the underlying data distribution; discriminative clustering, on the other hand, emphasizes on finding partition boundaries. In this paper, we take the advantages of both models by coupling the two paradigms through feature mapping derived from linearizing Bayesian classifiers. Such the feature mapping strategy maps nonlinear boundaries of generative clustering to linear ones in the feature space where we expl

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Chen, Zhijun, Jingming Zhang, Yishi Zhang, and Zihao Huang. "Traffic Accident Data Generation Based on Improved Generative Adversarial Networks." Sensors 21, no. 17 (2021): 5767. http://dx.doi.org/10.3390/s21175767.

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For urban traffic, traffic accidents are the most direct and serious risk to people’s lives, and rapid recognition and warning of traffic accidents is an important remedy to reduce their harmful effects. However, research scholars are often confronted with the problem of scarce and difficult-to-collect accident data resources for traffic accident scenarios. Therefore, in this paper, a traffic data generation model based on Generative Adversarial Networks (GAN) is developed. To make GAN applicable to non-graphical data, we improve the generator network structure of the model and used the genera

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Ojo, Mike O., and Azlan Zahid. "Improving Deep Learning Classifiers Performance via Preprocessing and Class Imbalance Approaches in a Plant Disease Detection Pipeline." Agronomy 13, no. 3 (2023): 887. http://dx.doi.org/10.3390/agronomy13030887.

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The foundation of effectively predicting plant disease in the early stage using deep learning algorithms is ideal for addressing food insecurity, inevitably drawing researchers and agricultural specialists to contribute to its effectiveness. The input preprocessor, abnormalities of the data (i.e., incomplete and nonexistent features, class imbalance), classifier, and decision explanation are typical components of a plant disease detection pipeline based on deep learning that accepts an image as input and outputs a diagnosis. Data sets related to plant diseases frequently display a magnitude im

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