Showing 1-25 of 2,016 resultsfor "Index Terms":Wasserstein Generative Adversarial Networks
"Index Terms":Wasserstein Generative Adversarial Networks
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Enhanced Conditions Based Deep Convolutional Generative Adversarial Networks
2021 IEEE 10th Global Conference on Consumer Electronics (GCCE)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
Generative adversarial network (GAN) is a prevalent generative model. While it is effective, it has been shown to be very hard to train in practice. This work demonstrates how an improvement to the GAN framework can be used in a stable training, and in a conditional manner able to restrict their generation according to some alternate information such as a class label. Additionally, we explore diff...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
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
Building footprint information is an essential ingredient for 3-D reconstruction of urban models. The automatic generation of building footprints from satellite images presents a considerable challenge due to the complexity of building shapes. In this work, we have proposed improved generative adversarial networks (GANs) for the automatic generation of building footprints from satellite images. We...Show More
Generation of Synthetic Ampacity and Electricity Pool Prices using Generative Adversarial Networks
2021 IEEE Electrical Power and Energy Conference (EPEC)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
This work explores the generation of synthetic time-series of dynamic thermal line rating data and Alberta Electric System Operator's hourly pool price data using Wasserstein Generative Adversarial Networks, as part of a larger study on transmission line reliability. The generation of synthetic data is required due to a limited size of the available dataset. Synthetic data can aid in training deep...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
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
Exchange Rate Forecasting via Generative Adversarial Network (GAN)
2024 IEEE International Conference on Computing, Applications and Systems (COMPAS)
Year: 2024 | Conference Paper |
Forecasting is an exciting topic in every field of study. Currency exchange rate Forecasting is a typical time series prediction problem which has been solved by different time-series models. Deep learning has been widely used in time series forecasting. Generative Adversarial Networks (GAN) has proved to be a powerful machine learning tool in image data analysis and generation. In this paper, we ...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
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
Building Footprint Generation Using Improved Generative Adversarial Networks
Building footprint information is an essential ingredient for 3-D reconstruction of urban models. The automatic generation of building footprints from satellite images presents a considerable challenge due to the complexity of building shapes. In this letter, we have proposed improved generative adversarial networks (GANs) for the automatic generation of building footprints from satellite images. ...Show More
CNN-Based Metrics for Performance Evaluation of Generative Adversarial Networks
Adarsh Prasad Behera;Satya Prakash;Siddhant Khanna;Shivangi Nigam;Shekhar Verma
In this work, we propose two convolutional neural network (CNN) based metrics, classification score (CS) and distribution score (DS), for performance evaluation of generative adversarial networks (GANs). Though GAN-generated images can be evaluated through manual assessment of visual fidelity, it is prolonged, subjective, challenging, tiresome, and can be misleading. Existing quantitative methods ...Show More
Forecasting Renewable Energy Generation Scenarios Based on Multi-Agent Diverse GANs
2020 IEEE Sustainable Power and Energy Conference (iSPEC)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
In this paper, we propose an approach for renewable energy scenario forecasts based on an improved generative adversarial networks (GANs) with multiple generators. Basically, the proposed approach can capture almost all the renewable scenario patterns from historical measurements and generate more accurate future scenarios without the knowledge of any explicit model. More specifically, Multi-Agent...Show More
Brain Tumour Segmentation Using Wasserstein Generative Adversarial Networks(WGANs)
2023 7th International Conference on Trends in Electronics and Informatics (ICOEI)
Year: 2023 | Conference Paper |
The majority of health-related applications use technology and require a lot of data to operate. Even more, information is required for brain tumor segmentation, but many people lack access to it because of privacy laws governing medical information. Therefore, this study utilizes GAN technology, which creates synthetic images to resolve this issue. AGGrGAN is used for aggregation and discriminati...Show More
Generative Adversarial Networks based image generation of insulator
Zhijun Liu;Haifeng Qiu;Liguo Weng;Man Luo;Xiaowei Zhu;Yanghui Zhang
2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC)
Year: 2022 | Conference Paper |
In recent years, the combination of artificial intelligence and power systems has been increasing. However, the collection of image datasets of power equipment is limited by places and environments, so the number of collected datasets is relatively small, which makes it unable to provide sufficient data support for specific applications. We propose to use Generative Adversarial Networks to generat...Show More
False Data Injection Attacks Based on Least Squares Generative Adversarial Networks with Reconstruction Loss
2024 IEEE Power & Energy Society General Meeting (PESGM)
Year: 2024 | Conference Paper |
Cybersecurity is increasingly vital in power systems, particularly with the rise of Internet of Things (IoT) devices. The integration of these devices amplifies the system’s exposure to threats like False Data Injection Attacks (FDIA). This work proposes a Generative Adversarial Networks (GANs) framework for generating FDIA against power system state estimation from the attacker’s perspective. Spe...Show More
HRRP Data Augmentation Using Generative Adversarial Networks
2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)
Year: 2021 | Conference Paper |
Cited by: Papers (6)
In radar automatic target recognition, high resolution range profile (HRRP) can promise satisfactory performance by deep learning when the training samples are affluent. Actually, it is difficult to acquire HRRP samples in battlefield environment. An approach using generative adversarial network (GAN) to augment HRRP data is proposed to deal with the lack of data. There are four GAN models adopted...Show More
Deep learning models have shown excellent performance on several problems, but their training process often requires a great amount of computational resources, which limits its applications. To solve this problem, a common method is to use data augmentation. Specifically, augmentation methods based on GANs have been greatly studied in recent researches. To be specific, we propose a novel generativ...Show More
A Novel GAN Architecture Reconstructed Using Bi-LSTM and Style Transfer for PV Temporal Dynamics Simulation
The stochastic production simulation of photovoltaic (PV) power is crucial for the analysis of power balance in power planning, annual or monthly operational planning, and long-term transactions in the electricity market, especially in power systems with a high share of PVs. To model the uncertainty and temporal characteristics inherent in PV power, this letter introduces the style transfer and in...Show More
Conditional Wasserstein GAN for Energy Load Forecasting in Large Buildings
2022 International Joint Conference on Neural Networks (IJCNN)
Year: 2022 | Conference Paper |
Cited by: Papers (4)
Energy forecasting is necessary for planning electricity consumption, and large buildings play a huge role when making these predictions. Because of its importance, numerous methods to predict the buildings' energy load have appeared during the last decades, remaining an open area of research. In recent years, traditional machine learning techniques such as Random Forest, K-Nearest Neighbors, and ...Show More
Robust Speech Recognition Using Generative Adversarial Networks
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2018 | Conference Paper |
This paper describes a general, scalable, end-to-end framework that uses the generative adversarial network (GAN) objective to enable robust speech recognition. Encoders trained with the proposed approach enjoy improved invariance by learning to map noisy audio to the same embedding space as that of clean audio. Unlike previous methods, the new framework does not rely on domain expertise or strong...Show More
Defense against adversarial attacks using DRAGAN
2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
In recent years, deep neural networks have been used in a wide range of applications such as machine vision, speech recognition, natural language processing, etc., and have achieved significant success, however, these networks are vulnerable to adversarial attacks. This has raised concerns about the security of these networks. In this paper, we are going to use Generative Adversarial Networks (GAN...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
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
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