Relevant bibliographies by topics / Generative adversarial model

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Generative adversarial model'

Author: Grafiati
Published: 22 February 2023
Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Generative adversarial model.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Generative adversarial model"

1

Huo, Lin, Huanchao Qi, Simiao Fei, Cong Guan, and Ji Li. "A Generative Adversarial Network Based a Rolling Bearing Data Generation Method Towards Fault Diagnosis." Computational Intelligence and Neuroscience 2022 (July 13, 2022): 1–21. http://dx.doi.org/10.1155/2022/7592258.

Full text
Abstract:
As a new generative model, the generative adversarial network (GAN) has great potential in the accuracy and efficiency of generating pseudoreal data. Nowadays, bearing fault diagnosis based on machine learning usually needs sufficient data. If enough near-real data can be generated in the case of insufficient samples in the actual operating condition, the effect of fault diagnosis will be greatly improved. In this study, a new rolling bearing data generation method based on the generative adversarial network (GAN) is proposed, which can be trained adversarially and jointly via a learned embedd
APA, Harvard, Vancouver, ISO, and other styles
2

Dingeto, Hiskias, and Juntae Kim. "Universal Adversarial Training Using Auxiliary Conditional Generative Model-Based Adversarial Attack Generation." Applied Sciences 13, no. 15 (2023): 8830. http://dx.doi.org/10.3390/app13158830.

Full text
Abstract:
While Machine Learning has become the holy grail of modern-day computing, it has many security flaws that have yet to be addressed and resolved. Adversarial attacks are one of these security flaws, in which an attacker appends noise to data samples that machine learning models take as input with the aim of fooling the model. Various adversarial training methods have been proposed that augment adversarial examples in the training dataset for defense against such attacks. However, a general limitation exists where a robust model can only protect itself against adversarial attacks that are known
APA, Harvard, Vancouver, ISO, and other styles
3

Iranmanesh, Seyed Mehdi, and Nasser M. Nasrabadi. "HGAN: Hybrid generative adversarial network." Journal of Intelligent & Fuzzy Systems 40, no. 5 (2021): 8927–38. http://dx.doi.org/10.3233/jifs-201202.

Full text
Abstract:
In this paper, we present a simple approach to train Generative Adversarial Networks (GANs) in order to avoid a mode collapse issue. Implicit models such as GANs tend to generate better samples compared to explicit models that are trained on tractable data likelihood. However, GANs overlook the explicit data density characteristics which leads to undesirable quantitative evaluations and mode collapse. To bridge this gap, we propose a hybrid generative adversarial network (HGAN) for which we can enforce data density estimation via an autoregressive model and support both adversarial and likelih
APA, Harvard, Vancouver, ISO, and other styles
4

Deupa, Abhishek, Garima Saha, Nikhil S. Sharma, and Virendra Pal Singh. "Generative Adversarial Network Based Music Generation." Far Western Review 2, no. 1 (2024): 1–25. http://dx.doi.org/10.3126/fwr.v2i1.70491.

Full text
Abstract:
Music has been an integral part of human civilization personally and culturally. Historically, music has been generated using various instruments, or natural sounds like water drops, or unconventional musical instruments like metal or glass-wares. At present, technologies like Musical Instrument Digital Interface (MIDI) are used to generate music electronically. This research investigates the use of Generative Adversarial Networks (GANs) for beginner-friendly music production. This model uses Long Short-Term Memory (LSTM) generator and Patch GAN as discriminator for the GAN architecture. The g
APA, Harvard, Vancouver, ISO, and other styles
5

Yin, Haiyan, Dingcheng Li, Xu Li, and Ping Li. "Meta-CoTGAN: A Meta Cooperative Training Paradigm for Improving Adversarial Text Generation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 9466–73. http://dx.doi.org/10.1609/aaai.v34i05.6490.

Full text
Abstract:
Training generative models that can generate high-quality text with sufficient diversity is an important open problem for Natural Language Generation (NLG) community. Recently, generative adversarial models have been applied extensively on text generation tasks, where the adversarially trained generators alleviate the exposure bias experienced by conventional maximum likelihood approaches and result in promising generation quality. However, due to the notorious defect of mode collapse for adversarial training, the adversarially trained generators face a quality-diversity trade-off, i.e., the g
APA, Harvard, Vancouver, ISO, and other styles
6

Imamverdiyev, Yadigar, and Firangiz Musayeva. "Analysis of generative adversarial networks." Problems of Information Technology 13, no. 1 (2022): 20–27. http://dx.doi.org/10.25045/jpit.v13.i1.03.

Full text
Abstract:
Recently, a lot of research has been done on the use of generative models in the field of computer vision and image classification. At the same time, effective work has been done with the help of an environment called generative adversarial networks, such as video generation, music generation, image synthesis, text-to-image conversion. Generative adversarial networks are artificial intelligence algorithms designed to solve the problems of generative models. The purpose of the generative model is to study the set of training patterns and their probable distribution. The article discusses genera
APA, Harvard, Vancouver, ISO, and other styles
7

Babu, Niby, Varghese S. Chooralil, Jucy Vareed, and Hrudya K.P. "ETHICS AND FAIRNESS IN GENERATIVE AI USING MITIGATING BIAS IN LARGE LANGUAGE MODELS USING ADVERSARIAL TRAINING." ICTACT Journal on Soft Computing 15, no. 3 (2025): 3598–607. https://doi.org/10.21917/ijsc.2025.0500.

Full text
Abstract:
Generative AI has revolutionized natural language processing (NLP) by enabling the creation of coherent and contextually relevant text. However, these models are susceptible to biases embedded in training datasets, leading to ethical concerns about fairness and equitable representation. This problem becomes critical in applications such as recruitment, healthcare, and education, where biased decisions can exacerbate social inequalities. Addressing these challenges requires robust methodologies to detect and mitigate bias in large language models. This study explores adversarial training as a m
APA, Harvard, Vancouver, ISO, and other styles
8

Rohith, Vallabhaneni. "Evaluating Transferability of Attacks across Generative Models." Engineering and Technology Journal 9, no. 06 (2024): 4261–67. https://doi.org/10.5281/zenodo.12223027.

Full text
Abstract:
The need for adversarial sample transferability is to attack black-box deep learning models. Whereas much recent work focuses on making untargeted adversarial attacks more transferable, there has been scarce research on the creation of transferable targeted adversarial instances that can trick models into believing they are of a particular class. The present transferable targeted adversarial attacks are not transferable since they cannot sufficiently define the distribution of target classes. In this paper, we propose a generative adversarial training system consisting of a feature-label dual
APA, Harvard, Vancouver, ISO, and other styles
9

Kumar, Krishna, Hardwari Lal Mandoria, Rajeev Singh, Shri Prakash Dwivedi, and Paras N. "Malware Detection and Classification using Generative Adversarial Network." International Journal of Computer Science and Information Technology 16, no. 5 (2024): 93–110. http://dx.doi.org/10.5121/ijcsit.2024.16508.

Full text
Abstract:
The Generative Adversarial Networks (GANs) are playing a crucial role in deep-learning-based malware classification to overcome the dataset imbalance and unseen malware. The Generative AI is preferably used in many applications, such as improving image resolution and generating audio, video, and text. The cybercriminals are also using the Generative AI for generating the malware and deepfake videos to harm the targeted person or device. By generating the synthetic data, it makes the deep learning model more robust to detect such types of unseen and adversarial attacks. This work utilizes GANs
APA, Harvard, Vancouver, ISO, and other styles
10

Dewi, Christine, Rung-Ching Chen, Yan-Ting Liu, and Hui Yu. "Various Generative Adversarial Networks Model for Synthetic Prohibitory Sign Image Generation." Applied Sciences 11, no. 7 (2021): 2913. http://dx.doi.org/10.3390/app11072913.

Full text
Abstract:
A synthetic image is a critical issue for computer vision. Traffic sign images synthesized from standard models are commonly used to build computer recognition algorithms for acquiring more knowledge on various and low-cost research issues. Convolutional Neural Network (CNN) achieves excellent detection and recognition of traffic signs with sufficient annotated training data. The consistency of the entire vision system is dependent on neural networks. However, locating traffic sign datasets from most countries in the world is complicated. This work uses various generative adversarial networks
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Generative adversarial model"

1

Cabezas, Rodríguez Juan Pablo. "Generative adversarial network based model for multi-domain fault diagnosis." Tesis, Universidad de Chile, 2019. http://repositorio.uchile.cl/handle/2250/170996.

Full text
Abstract:
Memoria para optar al título de Ingeniero Civil Mecánico<br>Con el uso de las redes neuronal profundas ganando terreno en el área de PHM, los sensores disminuyendo progresivamente su precio y mejores algoritmos, la falta de datos se ha vuelto un problema principal para los modelos enfocados en datos. Los datos etiquetados y aplicables a escenarios específicos son, en el mejor de los casos, escasos. El objetivo de este trabajo es desarrollar un método para diagnosticas el estado de un rodamiento en situaciones con datos limitados. Hoy en día la mayoría de las técnicas se enfocan en mejora
APA, Harvard, Vancouver, ISO, and other styles
2

Oskarsson, Joel. "Probabilistic Regression using Conditional Generative Adversarial Networks." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166637.

Full text
Abstract:
Regression is a central problem in statistics and machine learning with applications everywhere in science and technology. In probabilistic regression the relationship between a set of features and a real-valued target variable is modelled as a conditional probability distribution. There are cases where this distribution is very complex and not properly captured by simple approximations, such as assuming a normal distribution. This thesis investigates how conditional Generative Adversarial Networks (GANs) can be used to properly capture more complex conditional distributions. GANs have seen gr
APA, Harvard, Vancouver, ISO, and other styles
3

Lenninger, Movitz. "Generative adversarial networks as integrated forward and inverse model for motor control." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-220535.

Full text
Abstract:
Internal models are believed to be crucial components in human motor control. It has been suggested that the central nervous system (CNS) uses forward and inverse models as internal representations of the motor systems. However, it is still unclear how the CNS implements the high-dimensional control of our movements. In this project, generative adversarial networks (GAN) are studied as a generative model of movement data. It is shown that, for a relatively small number of effectors, it is possible to train a GAN which produces new movement samples that are plausible given a simulator environme
APA, Harvard, Vancouver, ISO, and other styles
4

Wu, Xinheng. "A Deep Unsupervised Anomaly Detection Model for Automated Tumor Segmentation." Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/22502.

Full text
Abstract:
Many researches have been investigated to provide the computer aided diagnosis (CAD) automated tumor segmentation in various medical images, e.g., magnetic resonance (MR), computed tomography (CT) and positron-emission tomography (PET). The recent advances in automated tumor segmentation have been achieved by supervised deep learning (DL) methods trained on large labelled data to cover tumor variations. However, there is a scarcity in such training data due to the cost of labeling process. Thus, with insufficient training data, supervised DL methods have difficulty in generating effective feat
APA, Harvard, Vancouver, ISO, and other styles
5

Nilsson, Mårten. "Augmenting High-Dimensional Data with Deep Generative Models." Thesis, KTH, Robotik, perception och lärande, RPL, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233969.

Full text
Abstract:
Data augmentation is a technique that can be performed in various ways to improve the training of discriminative models. The recent developments in deep generative models offer new ways of augmenting existing data sets. In this thesis, a framework for augmenting annotated data sets with deep generative models is proposed together with a method for quantitatively evaluating the quality of the generated data sets. Using this framework, two data sets for pupil localization was generated with different generative models, including both well-established models and a novel model proposed for this pu
APA, Harvard, Vancouver, ISO, and other styles
6

Yuan, Mengfei. "Machine Learning-Based Reduced-Order Modeling and Uncertainty Quantification for "Structure-Property" Relations for ICME Applications." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555580083945861.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Ljung, Mikael. "Synthetic Data Generation for the Financial Industry Using Generative Adversarial Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301307.

Full text
Abstract:
Following the introduction of new laws and regulations to ensure data protection in GDPR and PIPEDA, interests in technologies to protect data privacy have increased. A promising research trajectory in this area is found in Generative Adversarial Networks (GAN), an architecture trained to produce data that reflects the statistical properties of its underlying dataset without compromising the integrity of the data subjects. Despite the technology’s young age, prior research has made significant progress in the generation process of so-called synthetic data, and the current models can generate i
APA, Harvard, Vancouver, ISO, and other styles
8

Tang, Hao. "Learning to Generate Things and Stuff: Guided Generative Adversarial Networks for Generating Human Faces, Hands, Bodies, and Natural Scenes." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/306790.

Full text
Abstract:
In this thesis, we mainly focus on image generation. However, one can still observe unsatisfying results produced by existing state-of-the-art methods. To address this limitation and further improve the quality of generated images, we propose a few novel models. The image generation task can be roughly divided into three subtasks, i.e., person image generation, scene image generation, and cross-modal translation. Person image generation can be further divided into three subtasks, namely, hand gesture generation, facial expression generation, and person pose generation. Meanwhile, scene image
APA, Harvard, Vancouver, ISO, and other styles
9

Tang, Hao. "Learning to Generate Things and Stuff: Guided Generative Adversarial Networks for Generating Human Faces, Hands, Bodies, and Natural Scenes." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/306790.

Full text
Abstract:
In this thesis, we mainly focus on image generation. However, one can still observe unsatisfying results produced by existing state-of-the-art methods. To address this limitation and further improve the quality of generated images, we propose a few novel models. The image generation task can be roughly divided into three subtasks, i.e., person image generation, scene image generation, and cross-modal translation. Person image generation can be further divided into three subtasks, namely, hand gesture generation, facial expression generation, and person pose generation. Meanwhile, scene image
APA, Harvard, Vancouver, ISO, and other styles
10

Lindqvist, Niklas. "Automatic Question Paraphrasing in Swedish with Deep Generative Models." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-294320.

Full text
Abstract:
Paraphrase generation refers to the task of automatically generating a paraphrase given an input sentence or text. Paraphrase generation is a fundamental yet challenging natural language processing (NLP) task and is utilized in a variety of applications such as question answering, information retrieval, conversational systems etc. In this study, we address the problem of paraphrase generation of questions in Swedish by evaluating two different deep generative models that have shown promising results on paraphrase generation of questions in English. The first model is a Conditional Variational
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Generative adversarial model"

1

Ahirwar, Kailash. Generative Adversarial Networks Projects: Build Next-Generation Generative Models Using TensorFlow and Keras. Packt Publishing, Limited, 2019.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Generative Adversarial Networks Cookbook: Over 100 Recipes to Build Generative Models Using Python, TensorFlow, and Keras. de Gruyter GmbH, Walter, 2018.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Kalin, Josh. Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using Python, TensorFlow, and Keras. Packt Publishing, 2018.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Walters, Greg, and John Hany. Hands-On Generative Adversarial Networks with Pytorch 1. x: Implement Next-Generation Neural Networks to Build Powerful GAN Models Using Python. Packt Publishing, Limited, 2019.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Sangeetha, V., and S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.

Full text
Abstract:
Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip it
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Generative adversarial model"

1

Ghayoumi, Mehdi. "Model Evaluation." In Generative Adversarial Networks in Practice. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003281344-3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Yuan, Yixiao, Yawen Huang, and Yi Zhou. "Rethinking a Unified Generative Adversarial Model for MRI Modality Completion." In Deep Generative Models. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53767-7_14.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Wang, Xiaoyin, Shuo Lv, Jiaze Sun, and Shuyan Wang. "Adversarial Attacks Medical Diagnosis Model with Generative Adversarial Networks." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89698-0_69.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Hafsi, H., A. Ghazdali, H. Khalfi, and N. Lamghari. "Unsupervising Denoising Model Based Generative Adversarial Networks." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-29313-9_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Wang, Donghua, Rangding Wang, Li Dong, Diqun Yan, and Yiming Ren. "Efficient Generation of Speech Adversarial Examples with Generative Model." In Digital Forensics and Watermarking. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69449-4_19.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Öngün, Cihan, and Alptekin Temizel. "Paired 3D Model Generation with Conditional Generative Adversarial Networks." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11009-3_29.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Xu, Lei, Zhentao Liu, Peng Liu, and Liyan Cai. "A Low Spectral Bias Generative Adversarial Model for Image Generation." In Communications in Computer and Information Science. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5194-7_26.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Zhang, Jinzhihao, Jia Yang, and Weiqi Zhou. "Malicious Code Detection Based on Generative Adversarial Model." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4566-4_12.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Wang, Yubo, Hui He, Peng Zhang, Yuanchi Ma, Zhongxiang Lei, and Zhendong Niu. "An Adversarial Attack Method for Multivariate Time Series Classification Based on AdvGAN." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2409-6_19.

Full text
Abstract:
Abstract Considering the complexity of time series data and real-world applications, multivariate time series classification models are vulnerable to adversarial attacks. Although existing white-box attack strategies have made progress in generating adversarial samples, they rely on access to the target model’s parameters, training data, and gradients. Therefore, we apply AdvGAN framework for multivariate time series classification. AdvGAN is designed as a framework based on Generative Adversarial Networks (GANs), encompassing a generator, discriminator. The generator creates multivariate pert
APA, Harvard, Vancouver, ISO, and other styles
10

Tariq, Usama, Rizwan Qureshi, Anas Zafar, et al. "Brain Tumor Synthetic Data Generation with Adaptive StyleGANs." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_12.

Full text
Abstract:
AbstractGenerative models have been very successful over the years and have received significant attention for synthetic data generation. As deep learning models are getting more and more complex, they require large amounts of data to perform accurately. In medical image analysis, such generative models play a crucial role as the available data is limited due to challenges related to data privacy, lack of data diversity, or uneven data distributions. In this paper, we present a method to generate brain tumor MRI images using generative adversarial networks. We have utilized StyleGAN2 with ADA
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Generative adversarial model"

1

Deba, El Abbassia, Abdelouadoud Sadeuk Ben Abbas, and Karima Berramla. "Synthetic Model Generation from Metamodel Using Conditional Tabular Generative Adversarial Network." In 2024 4th International Conference on Embedded & Distributed Systems (EDiS). IEEE, 2024. https://doi.org/10.1109/edis63605.2024.10783213.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Chen, Yuheng, Michael Bezick, Blake Wilson, et al. "Advancing photonic design with topological latent diffusion generative model." In Frontiers in Optics. Optica Publishing Group, 2024. https://doi.org/10.1364/fio.2024.jw5a.58.

Full text
Abstract:
Conventional photonic design often relies on inefficient trial-and-error. The proposed Topological Latent Diffusion Model (TLDM) captures high-level features from topology dataset and outperforms state-of-the-art Generative Adversarial Networks and Variational Autoencoders methods in high-efficiency metasurface design.
APA, Harvard, Vancouver, ISO, and other styles
3

Qu, Xiwen, and Hao Chen. "End-to-end generative adversarial network model for in-air handwritten character generation." In Seventeenth International Conference on Digital Image Processing (ICDIP 2025), edited by Xudong Jiang, Jindong Tian, Ting-Chung Poon, and Zhaohui Wang. SPIE, 2025. https://doi.org/10.1117/12.3072854.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Chent, Ying, Weihao Guo, Pingyuan Ge, Xiaoji Ma, and Yuqing Zhang. "Model Extraction Attacks on Text-to-Image Generative Adversarial Networks." In 2024 IEEE Cyber Science and Technology Congress (CyberSciTech). IEEE, 2024. https://doi.org/10.1109/cyberscitech64112.2024.00050.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Yin, Yinghui, Xu Li, Zitong Yan, and Xinlong Wang. "Adversarial Generative Model for Cross-Domain Aspect-Based Sentiment Analysis." In 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS). IEEE, 2024. http://dx.doi.org/10.1109/docs63458.2024.10704498.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Rathod, Darshanbhai Maheshbhai, and Rajitha B. "Enhancing Traffic Visibility Using Self Attention Based Generative Adversarial Network Model." In 2024 IEEE Students Conference on Engineering and Systems (SCES). IEEE, 2024. http://dx.doi.org/10.1109/sces61914.2024.10652525.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Yang, Siwen, and Hong Chen. "Traffic Anomaly Detection Model Integrating Conditional Generative Adversarial Network and WaveNet." In 2024 IEEE 2nd International Conference on Sensors, Electronics and Computer Engineering (ICSECE). IEEE, 2024. http://dx.doi.org/10.1109/icsece61636.2024.10729583.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Sun, Yiyun. "Dugdale-GAN: Physical Dugdale Model Integrated Generative Adversarial Network for High-quality Crack Image Generation." In 2025 IEEE 5th International Conference on Power, Electronics and Computer Applications (ICPECA). IEEE, 2025. https://doi.org/10.1109/icpeca63937.2025.10928903.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Chu, Shijie. "Leverage of generative adversarial model for boosting exploration in deep reinforcement learning." In 2024 6th International Conference on Internet of Things, Automation and Artificial Intelligence (IoTAAI). IEEE, 2024. http://dx.doi.org/10.1109/iotaai62601.2024.10692624.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Jianshe, Liu, Zhu Guangping, and Yin Jingwei. "Optimized Auxiliary Classifier Generative Adversarial Network Model for Underwater Acoustic Source Ranging." In 2024 OES China Ocean Acoustics (COA). IEEE, 2024. http://dx.doi.org/10.1109/coa58979.2024.10723465.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Generative adversarial model"

1

Huang, Lei, Meng Song, Hui Shen, et al. Deep learning methods for omics data imputation. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48221.

Full text
Abstract:
One common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes. Instead of discarding missing values and related data, imputation approaches offer an alternative means of handling missing data. However, the imputation of missing omics data is a non-trivial task. Difficulties mainly come from high dimensionality, non-linear or nonmonotonic relationships within features, technical variations introduced by sampling methods, sample heterogeneity, and the non-random missingness mechanism. Several advan
APA, Harvard, Vancouver, ISO, and other styles
2

Pasupuleti, Murali Krishna. Securing AI-driven Infrastructure: Advanced Cybersecurity Frameworks for Cloud and Edge Computing Environments. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv225.

Full text
Abstract:
Abstract: The rapid adoption of artificial intelligence (AI) in cloud and edge computing environments has transformed industries by enabling large-scale automation, real-time analytics, and intelligent decision-making. However, the increasing reliance on AI-powered infrastructures introduces significant cybersecurity challenges, including adversarial attacks, data privacy risks, and vulnerabilities in AI model supply chains. This research explores advanced cybersecurity frameworks tailored to protect AI-driven cloud and edge computing environments. It investigates AI-specific security threats,
APA, Harvard, Vancouver, ISO, and other styles
3

Hwang, Tim. Deepfakes: A Grounded Threat Assessment. Center for Security and Emerging Technology, 2020. http://dx.doi.org/10.51593/20190030.

Full text
Abstract:
The rise of deepfakes could enhance the effectiveness of disinformation efforts by states, political parties and adversarial actors. How rapidly is this technology advancing, and who in reality might adopt it for malicious ends? This report offers a comprehensive deepfake threat assessment grounded in the latest machine learning research on generative models.
APA, Harvard, Vancouver, ISO, and other styles
4

Pasupuleti, Murali Krishna. Decentralized Creativity: AI-Infused Blockchain for Secure and Transparent Digital Innovation. National Education Services, 2025. https://doi.org/10.62311/nesx/rrvi125.

Full text
Abstract:
Abstract The convergence of artificial intelligence (AI) and blockchain technology is transforming the creative economy by enabling secure, transparent, and decentralized innovation in digital content creation, intellectual property management, and monetization. Traditional creative industries are often constrained by centralized platforms, opaque copyright enforcement, and unfair revenue distribution, which limit the autonomy and financial benefits of creators. By leveraging blockchain’s immutable ledger, smart contracts, and non-fungible tokens (NFTs), digital assets can be authenticated, to
APA, Harvard, Vancouver, ISO, and other styles
5

A Decision-Making Method for Connected Autonomous Driving Based on Reinforcement Learning. SAE International, 2020. http://dx.doi.org/10.4271/2020-01-5154.

Full text
Abstract:
At present, with the development of Intelligent Vehicle Infrastructure Cooperative Systems (IVICS), the decision-making for automated vehicle based on connected environment conditions has attracted more attentions. Reliability, efficiency and generalization performance are the basic requirements for the vehicle decision-making system. Therefore, this paper proposed a decision-making method for connected autonomous driving based on Wasserstein Generative Adversarial Nets-Deep Deterministic Policy Gradient (WGAIL-DDPG) algorithm. In which, the key components for reinforcement learning (RL) model
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Generative adversarial model' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography