Showing 1-25 of 4,874 resultsfor "Index Terms":Generative Adversarial Network Architecture
"Index Terms":Generative Adversarial Network Architecture
- Sort
Get notified when new research is published matching your search criteria.
Sort By
Results
Generating Images Using Vanilla Generative Adversarial Networks
Avantika Bisht;Khushi Rawat;Jai Prakash Bhati;S. Vats;V. Sharma;Sunny Singh
2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS)
Year: 2024 | Conference Paper |
In recent years, GANs have become substantial in computer vision. The goal of this paper is to explore the application of GANs for generating synthetic MNIST data and to compare the images of GAN-generated data with the original MNIST data. The GAN model is trained in this study to produce handwritten digit images that resemble the images found in the MNIST database. In this study, a Vanilla GAN i...Show More
Generative Adversarial Networks in ECG Signal Denoising: A Survey
Mohamed Sraitih;Amir Hajjam El Hassani;Younes Jabrane;Emmanuel Andres
2024 10th International Conference on Optimization and Applications (ICOA)
Year: 2024 | Conference Paper |
In this survey, we present a thorough analysis of denoising ECG signal approaches using Generative Adversarial Networks (GANs). Our aim with this survey is to examine the most recent studies that have utilized different types of GANs architecture for removing various types of noise from ECG signals, ranging from real-world to synthetic noise. This review paper offers experts a clear understanding ...Show More
Evolution and effectiveness of loss functions in generative adversarial networks
Ali Syed Saqlain;Fang Fang;Tanvir Ahmad;Liyun Wang;Zain-ul Abidin
Recently, the evolution of Generative Adversarial Networks (GANs) has embarked on a journey of revolutionizing the field of artificial and computational intelligence. To improve the generating ability of GANs, various loss functions are introduced to measure the degree of similarity between the samples generated by the generator and the real data samples, and the effectiveness of the loss function...Show More
A comparison of Generative Adversarial Networks for image super-resolution
2022 IEEE Latin American Conference on Computational Intelligence (LA-CCI)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
This article presents a comparison of Generative Adversarial Networks for the image super-resolution problem. This is a relevant problem in several research areas and many real-world applications. The research consists of four steps: selecting successful Generative Adversarial Networks architectures, implementing two promising models, evaluating their image quality results, and analyzing their tra...Show More
Adversarial Synthesis based Data Augmentation for Speech Classification
Parth Shastri;Chirag Patil;Poorval Wanere;Srinivas Mahajan;Abhishek Bhatt
2022 International Conference on Signal and Information Processing (IConSIP)
Year: 2022 | Conference Paper |
Speech classification plays a vital role in modern audio processing, with the rise in technologies like home assistants and speech-based control devices. Deep learning-based algorithms have played a big role in developing such technologies. Deep learning algorithms are data-hungry and need large labelled datasets for classification. However, finding such labelled datasets is rare in the real world...Show More
Generative Adversarial Networks in Image Generation and Recognition
2023 International Conference on Computational Science and Computational Intelligence (CSCI)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Generative Adversarial Network (GAN) is a class of Generative Machine Learning frameworks, which has shown remarkable promise in the field of synthetic data generation. GANs consist of a generative model and a discriminative model working in a game like contest to generate data with high levels of accuracy. This paper delves into the applications of GANs in the field of Image Generation and Recogn...Show More
GAN Based Anomaly Detection in Medical Device and Failure Data
2025 6th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI)
Year: 2025 | Conference Paper |
In the era of modern health care, medical devices play a vital role henceforth upholding their operational reliability is pivotal for patient welfare. This paper anchors on detecting anomalies in medical device failure data by making use of Generative Adversarial Network (GAN), by signifying dosage related failures such as overdose, underdose and chronic dosing issues. The traditional anomaly dete...Show More
Assisting the Adversary to Improve GAN Training
2021 International Joint Conference on Neural Networks (IJCNN)
Year: 2021 | Conference Paper |
Some of the most popular methods for improving the stability and performance of GANs involve constraining or regularizing the discriminator. In this paper we consider a largely overlooked regularization technique which we refer to as the Adversary's Assistant (AdvAs). We motivate this using a different perspective to that of prior work. Specifically, we consider a common mismatch between theoretic...Show More
In this paper authors tries to show success of using ensemble of loss function on GAN network model. The model was able to generate quality synthetic data on MNIST handwritten dataset. The performance of the simple vanilla GAN model is evaluated for binary entropy loss and least square loss combined and compared for both of these loss functions separately. Instead of using only one single activati...Show More
ViT-R50 GAN: Vision Transformers Hybrid Model based Generative Adversarial Networks for Image Generation
2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
In recent years, the tremendous potential of GAN in image generation has been demonstrated. Transformer derived from the NLP field is also gradually applied in computer vision, and Vision Transformer performs well in image classification problems. In this paper, we design a ViT-based GAN architecture for image generation. We found that the Transformer-based generator did not perform well due to us...Show More
Word Level Font-to-Font Image Translation using Convolutional Recurrent Generative Adversarial Networks
Ankan Kumar Bhunia;Ayan Kumar Bhunia;Prithaj Banerjee;Aishik Konwer;Abir Bhowmick;Partha Pratim Roy;Umapada Pal
2018 24th International Conference on Pattern Recognition (ICPR)
Year: 2018 | Conference Paper |
Conversion of one font to another font is very useful in real life applications. In this paper, we propose a Convolutional Recurrent Generative model to solve the word level font transfer problem. Our network is able to convert the font style of any printed text images from its current font to the required font. The network is trained end-to-end for the complete word images. Thus it eliminates the...Show More
Anomaly Detection for Alzheimer’s Disease in Brain MRIs via Unsupervised Generative Adversarial Learning
Jean Nathan Cabreza;Geoffrey A. Solano;Sun Arthur Ojeda;Vincent Munar
2022 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)
Year: 2022 | Conference Paper |
Cited by: Papers (10)
Alzheimer’s disease (AD) is a neurodegenerative disease that results in cognitive decline, and even dementia, in patients. To diagnose AD, a combination of tools is typically used, with structural magnetic resonance imaging (sMRI) being one of them. sMRI images have mostly been used in supervised deep learning approaches, which requires large amounts of labeled data. To alleviate the need for labe...Show More
Exploring the Effects of Various Generative Adversarial Networks Techniques on Image Generation
2023 IEEE 11th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Generative Adversarial Networks (GANs) have rapidly risen to prominence in the sphere of deep learning. This is especially true when it comes to image generation, where GANs have displayed impressive capabilities. Over time, as researchers have grappled with the challenges posed by the original GAN model, a plethora of GAN variants have been introduced. These are tailored to counteract training in...Show More
Image generation with explicit condition or label generally works better than unconditional methods. In modern GAN frameworks, both generator and discriminator are formulated to model the conditional distribution of images given with labels. In this article, we provide an alternative formulation of GAN which models the joint distribution of images and labels. There are two advantages in this joint...Show More
Facial Image Generation using fully trained Generative Adversarial Network
2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA)
Year: 2024 | Conference Paper |
Recent advancements in facial image generation have been significant. However, many existing methods are constrained to generating face images solely from random noise, lacking the capability to synthesize images based on specific features. In this work, the issue of face synthesis from features is mapped, trying to produce facial images that have unique features that match pre-specified criteria....Show More
A Case Study of Generative Adversarial Networks for Procedural Synthesis of Original Textures in Video Games
2018 2nd National and 1st International Digital Games Research Conference: Trends, Technologies, and Applications (DGRC)
Year: 2018 | Conference Paper |
Cited by: Papers (14)
Procedural content generation is helping game developers to create significant quantity of high quality dynamic content for video games at a fraction of cost of the traditional methods. Procedural texture synthesis is a sub category of procedural content generation which helps video games to have significant variations in textures of the environments and the objects across the progress of the game...Show More
A Case Study of Generative Adversarial Networks for Procedural Synthesis of Original Textures in Video Games
Year: 2018 | Conference Paper |
Anti-forensics for JPEG decompression based on generative adversarial network
2024 5th International Conference on Computer Engineering and Application (ICCEA)
Year: 2024 | Conference Paper |
Eliminating artifacts left after decompression of JPEG images, especially those compressed at high quality factors, is a challenging issue in image anti-forensics. In this paper, JPEG decompression anti-forensics are modeled as an image-to-image translation problem, where a generative adversarial network framework is used to translate a JPEG decompressed image to a reconstructed one. Due to the in...Show More
Text To Image Conversion Using Generative Adversarial NetworK
Md. Shabber;Chityala Nandhini;Arra Pragathi;A Sathvika;R. Pitchai
2024 First International Conference on Technological Innovations and Advance Computing (TIACOMP)
Year: 2024 | Conference Paper |
Text-to-image synthesis is a task of making practical photos that fit the textual content descriptions. This is a hard hassle that requires both herbal language know-how and pc vision capabilities. Existing AI structures aren't capable of achieve this aim but. However, recent advances in deep mastering have enabled the development of powerful fashions which can study meaningful textual content cap...Show More
A White-Box Generator Membership Inference Attack Against Generative Models
Maryam Azadmanesh;Behrouz Shahgholi Ghahfarokhi;Maede Ashouri Talouki
2021 18th International ISC Conference on Information Security and Cryptology (ISCISC)
Year: 2021 | Conference Paper |
Cited by: Papers (3)
Using generative models to generate unlimited number of synthetic samples is a popular replacement of database sharing. When these models are built using sensitive data, the developers should ensure that the training dataset is appropriately protected. Hence, quantifying the privacy risk of these models is important. In this paper, we focus on evaluating privacy risk of publishing generator in gen...Show More
A Novel Framework for Semantic Image Segmentation using Generative Adversarial Networks and Neural Architecture Search
2021 10th International Conference on Information and Automation for Sustainability (ICIAfS)
Year: 2021 | Conference Paper |
Semantic segmentation is a fundamental task in image processing and computer vision domains. Therefore, it is a core component of most emerging industry applications such as medical imaging, autonomous driving, and agriculture. The last five years have witnessed a huge growth in the computer vision domain with the introduction of Generative Adversarial Networks (GANs) due to their ability to learn...Show More
ZeroNAS: Differentiable Generative Adversarial Networks Search for Zero-Shot Learning
Caixia Yan;Xiaojun Chang;Zhihui Li;Weili Guan;Zongyuan Ge;Lei Zhu;Qinghua Zheng
In recent years, remarkable progress in zero-shot learning (ZSL) has been achieved by generative adversarial networks (GAN). To compensate for the lack of training samples in ZSL, a surge of GAN architectures have been developed by human experts through trial-and-error testing. Despite their efficacy, however, there is still no guarantee that these hand-crafted models can consistently achieve good...Show More
Generative Adversarial Network Applications in Creating a Meta-Universe
Soheyla Amirian;Thiab R. Taha;Khaled Rasheed;Hamid R. Arabnia
2021 International Conference on Computational Science and Computational Intelligence (CSCI)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Generative Adversarial Networks (GANs) are machine learning methods that are used in many important and novel applications. For example, in imaging science, GANs are effectively utilized in generating image datasets, photographs of human faces, image and video captioning, image-to-image translation, text-to-image translation, video prediction, and 3D object generation to name a few. In this paper,...Show More
Adversarial Training Classifier for Imbalanced and Semi-Supervised Learning
2023 China Automation Congress (CAC)
Year: 2023 | Conference Paper |
The imbalanced learning research has made great progress due to the introduction of generative adversarial networks (GANs). However, most studies focus on combining GAN models with oversampling techniques which could potentially compromise the distribution of the original training dataset. This study presents a cost-sensitive classifier based on the adversarial training framework that can not only...Show More
Generative Adversarial Networks (GAN) for Arabic Calligraphy
Mahmood Abdulhameed Ahmed;Mohsen Ali;Jassim Ahmed Jassim;Hesham Mohamed Al-Ammal
2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Arabic calligraphy is one of the most aesthetic art forms in the world due to its variety and long history. However, generating calligraphic style is mainly done by human expert calligrapher (also known as Khattat) and has not been carried out by machine learning techniques. Generative adversarial networks (GAN) are deep learning tools that achieved outstanding results in the field of style transf...Show More
Evolutionary Architecture Search for Generative Adversarial Networks Based on Weight Sharing
Yu Xue;Weinan Tong;Ferrante Neri;Peng Chen;Tao Luo;Liangli Zhen;Xiao Wang
Generative adversarial networks (GANs) are a powerful generative technique but frequently face challenges with training stability. Network architecture plays a significant role in determining the final output of GANs, but designing a fine architecture demands extensive domain expertise. This article aims to address this issue by searching for high-performance generator’s architectures through neur...Show More
IEEE Account
Purchase Details
Profile Information
Need Help?
- US & Canada: +1 800 678 4333
- Worldwide: +1 732 981 0060
- Contact & Support
- About IEEE Xplore
- Contact Us
- Help
- Accessibility
- Terms of Use
- Nondiscrimination Policy
- Sitemap
- Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.