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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
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
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
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
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 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
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
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
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
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
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
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
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
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
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 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
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
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
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 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
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
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
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