Application Of Generative Adversarial Networks Research Articles

Advancements and Applications of Generative Adversarial Networks: A Comprehensive Review

Abstracts: Our paper offers a comprehensive exploration of Generative Adversarial Networks (GANs), tracing their evolution from Ian Goodfellow's seminal work to their current state-of-the-art status. Delving into the intricacies of GAN architecture and training dynamics, we illuminate their pivotal role in diverse applications such as image synthesis, style transfer, and text-toimage conversion. Through an exhaustive literature review, we dissect the progression of GAN architectures, from Vanilla GANs [1] to advanced variants like Progressive GANs [7] and StyleGANs [8], highlighting their techniques, contributions, and performance across benchmark datasets. Moreover, we confront challenges such as training instability and mode collapse, while also presenting a meticulously curated repository of contemporary generative model advancements. This repository encapsulates the cutting edge of GAN research, showcasing innovative approaches across domains ranging from financial forecasting to image restoration. Despite hurdles and ethical considerations, GANs persist as the vanguard of generative modeling, propelling forward the frontiers of artificial intelligence and creative synthesis.

A review of the applications of generative adversarial networks to structural and functional MRI based diagnostic classification of brain disorders.

Structural and functional MRI (magnetic resonance imaging) based diagnostic classification using machine learning has long held promise, but there are many roadblocks to achieving their potential. While traditional machine learning models suffered from their inability to capture the complex non-linear mapping, deep learning models tend to overfit the model. This is because there is data scarcity and imbalanced classes in neuroimaging; it is expensive to acquire data from human subjects and even more so in clinical populations. Due to their ability to augment data by learning underlying distributions, generative adversarial networks (GAN) provide a potential solution to this problem. Here, we provide a methodological primer on GANs and review the applications of GANs to classification of mental health disorders from neuroimaging data such as functional MRI and showcase the progress made thus far. We also highlight gaps in methodology as well as interpretability that are yet to be addressed. This provides directions about how the field can move forward. We suggest that since there are a range of methodological choices available to users, it is critical for users to interact with method developers so that the latter can tailor their development according to the users' needs. The field can be enriched by such synthesis between method developers and users in neuroimaging.

Application of generative adversarial networks in image, face reconstruction and medical imaging: challenges and the current progress

ABSTRACT In deep learning, GANs (Generative Adversarial Networks) are one of the prominent study areas due to their ability to generate synthetic data thereby solving the problem of the unavailability and the limited data sets. GAN is a framework of deep neural networks that can learn from a set of training data and generate new data with similar characteristics as the training data. In this review, the history of GAN, various types of GANs, objective functions, loss functions, and performance analysis done for GANs in various fields are also analysed. The main objective of the paper is to analyse the application of GANs in face restoration, and medical imaging including their evaluation metrics and data sets used. A deep review has been carried out on various types of GANs, their architecture, objective functions, and applications. This review focuses more on the medical applications using various types of GANs including image augmentation, disease detection, medical image enhancement, face restoration, detection, etc. The challenges, current progress, and future applications using various GANs are also discussed. This review clearly shows that the application of GANs has increased considerably and thus it proves a promising future in the field of deep learning.

Anomaly Detection using Generative Adversarial Networks Reviewing methodological progress and challenges

The applications of Generative Adversarial Networks (GANs) are just as diverse as their architectures, problem settings as well as challenges. A key area of research on GANs is anomaly detection where they are most often utilized when only the data of one class is readily available. In this work, we organize, summarize and compare key concepts and challenges of anomaly detection based on GANs. Common problems which have to be investigated to progress the applicability of GANs are identified and discussed. This includes stability and time requirements during training as well as inference, the restriction of the latent space to produce solely data from the normal class distribution, contaminated training data as well as the composition of the resulting anomaly detection score. We discuss the problems using existing work as well as possible (partial) solutions, including related work from similar areas of research such as related generative models or novelty detection. Our findings are also relevant for a variety of closely related generative modeling approaches, such as autoencoders, and are of interest for areas of research tangent to anomaly detection such as image inpainting or image translation.

Medical image super-resolution reconstruction: A comprehensive investigation of Generative Adversarial Networks

Medical images play a crucial role in modern healthcare diagnostics and treatment. However, many medical images suffer from limitations in resolution, potentially impeding a comprehensive understanding of a patient's condition by healthcare professionals. This comprehensive review delves into the applications of Generative Adversarial Networks (GANs) in medical image super-resolution reconstruction to address this challenge. In the Methods section, this paper first focused on the direction of medical image classification, including cell classification of histopathological images and synthetic data enhancement using GANs to improve liver lesion classification. Subsequently, this paper focused on the direction of medical image segmentation, looking into the use of Structure-Corrected Adversarial Networks (SCAN) for organ segmentation in chest radiographs and Deep Adversarial Networks for biomedical image segmentation using unannotated images. In the Applications and Discussion section, this paper thoroughly examined the current progress of GANs in telemedicine diagnosis and disease state generation and prediction. This paper emphasized the significant potential of GAN technology in telemedicine while outlining the current constraints and challenges. Furthermore, this paper highlighted the prospects of GANs in medical image super-resolution reconstruction and how they affect the discipline of medical imaging. This comprehensive review consolidates the latest research findings on GANs in medical image super-resolution reconstruction, underscoring their importance in the realm of healthcare. By critically analysing existing literature, this paper provides valuable insights for medical image analysts are researchers while inspiring future research directions and innovations.

Water depth prediction in combined sewer networks, application of generative adversarial networks

This paper addresses the pressing issue of combined sewer overflows (CSOs) in urban areas, which pose significant environmental and public health threats. CSOs occur when combined sewer systems become overwhelmed during heavy rainfall, leading to untreated sewage and stormwater being discharged into nearby water bodies. To effectively manage and mitigate CSO effects, accurate predictions of CSOs are crucial for real-time control measures. This study introduces an innovative approach that utilizes Generative Adversarial Networks (GANs) to augment data and improve the accuracy of data-driven models for predicting water depth in combined sewer systems. Apart from data augmentation, the paper addresses scenarios where rare patterns, such as extreme events, are infrequently observed regardless of dataset size. It proposes a specialized generative model based on GANs designed to augment datasets targeting these rare patterns. This innovation involves adding a tail-focused loss function to the adversarial objective of the GAN training pipeline. The research is exemplified through a case study in Kaiserslautern, Germany. The results indicate an improvement in water depth predictions. Also, it was found that the model trained solely with synthetic data is comparable to that trained with real data.Graphical

Application of Generative Adversarial Network in Image Color Correction

The popularity of electronic products has increased with the development of technology. Electronic devices allow people to obtain information through the transmission of images. However, color distortion can occur during the transmission process, which may hinder the usefulness of the images. To this end, a deep residual network and a deep convolutional network were used to define the generator and discriminator. Then, self-attention-enhanced convolution was applied to the generator network to construct an image resolution correction model based on coupled generative adversarial networks. On this basis, a generative network model integrating multi-scale features and contextual attention mechanism was constructed to achieve image restoration. Finally, performance and image restoration application tests were conducted on the constructed model. The test showed that when the coupled generative adversarial network was tested on the Set5 dataset, the image peak signal-to-noise ratio and image structure similarity values were 31.2575 and 0.8173. On the Set14 dataset, they were 30.8521 and 0.8079, respectively. The multi-scale feature-fusion algorithm was tested on the BSDS100 dataset with an image peak signal-to-noise ratio of 30.2541 and an image structure similarity value of 0.8352. Based on the data presented, it can be concluded that the image correction model constructed in this study has a strong image restoration ability. The reconstructed image has the highest similarity with the real high-resolution image and a low distortion rate. It can achieve the task of repairing problems such as color distortion during image transmission. In addition, this study can provide technical support for similar information correction and restoration work.

Applications of generative adversarial networks in materials science

AbstractGenerative adversarial networks (GANs), as a powerful tool for inverse materials discovery, are being increasingly applied in various fields of materials science. This review provides systematic investigations on the applications of GANs from a group of different aspects. The basic principles of GANs are first introduced; then a detailed review of GANs‐based studies regarding distinct scenarios across composition design, processing optimization, crystal structure search, microstructure characterization and defect detection is presented. At the end, several challenges and possible solutions are discussed and outlined. This overview highlights the efficacy of GANs in materials science, and may stimulate the further use of GANs for more intriguing achievements.

TRA-ACGAN: A motor bearing fault diagnosis model based on an auxiliary classifier generative adversarial network and transformer network

Motor bearing fault diagnosis is essential to guarantee production efficiency and avoid catastrophic accidents. Deep learning-based methods have been developed and widely used for fault diagnosis, and these methods have proven to be very effective in accurately diagnosing bearing faults. In this paper, study the application of generative adversarial networks (GANs) in motor bearing fault diagnosis to address the practical issue of insufficient fault data in industrial testing. Focus on the auxiliary classifier generative adversarial network (ACGAN), and the data expansion is carried out for small datasets. This paper present a novel transformer network and auxiliary classifier generative adversarial network (TRA-ACGAN) for motor bearing fault diagnosis, where the TRA-ACGAN combines an ACGAN with a transformer network to avoid the traditional iterative and convolutional structures. The attention mechanism is fully utilized to extract more effective features, and the dual-task coupling problem encountered in classical ACGANs is avoided. Experimental results with the CWRU dataset and the PU dataset in the field of motor bearing fault diagnosis demonstrate the suitability and superiority of the TRA-ACGAN.

Predicting materials properties with generative models: applying generative adversarial networks for heat flux generation

In the realm of materials science, the integration of machine learning techniques has ushered in a transformative era. This study delves into the innovative application of generative adversarial networks (GANs) for generating heat flux data, a pivotal step in predicting lattice thermal conductivity within metallic materials. Leveraging GANs, this research explores the generation of meaningful heat flux data, which has a high degree of similarity with that calculated by molecular dynamics simulations. This study demonstrates the potential of artificial intelligence (AI) in understanding the complex physical meaning of data in materials science. By harnessing the power of such AI to generate data that is previously attainable only through experiments or simulations, new opportunities arise for exploring and predicting properties of materials.

Attention-guided generator with dual discriminator GAN for real-time video anomaly detection

Detecting anomalies in videos presents a significant challenge in the field of video surveillance. The primary goal is identifying and detecting uncommon actions or events within a video sequence. The difficulty arises from the limited availability of video frames depicting anomalies and the ambiguous definition of anomaly. Based on extensive applications of Generative Adversarial Networks (GANs), which consist of a generator and a discriminator network, we propose an Attention-guided Generator with Dual Discriminator GAN (A2D-GAN) for real-time video anomaly detection (VAD). The generator network uses an encoder–decoder architecture with a multi-stage self-attention added to the encoder and multi-stage channel attention added to the decoder. The framework uses adversarial learning from noise and video frame reconstruction to enhance the generalization of the generator network. Also, of the dual discriminator in A2D-GAN, one discriminates between the reconstructed video frame and the real video frame, while the other discriminates between the reconstructed noise and the real noise. Exhaustive experiments and ablation studies on four benchmark video anomaly datasets, namely UCSD Peds, CUHK Avenue, ShanghaiTech, and Subway, demonstrate the effectiveness of the proposed A2D-GAN compared to other state-of-the-art methods. The proposed A2D-GAN model is robust and can detect anomalies in videos in real-time. The source code to replicate the results of the proposed A2D-GAN model is available at https://github.com/Rituraj-ksi/A2D-GAN.

Application of Generative Adversarial Networks for Virtual Silicon Data Generation and Design-Technology Co-Optimization: A Study on WAT and CP

Application of Generative Adversarial Networks for Virtual Silicon Data Generation and Design-Technology Co-Optimization: A Study on WAT and CP

The Applications of Generative Adversarial Networks in Architecture Design: A Systematic Review

The Applications of Generative Adversarial Networks in Architecture Design: A Systematic Review

Cardiac Magnetic Resonance Imaging Focus Generative Adversarial Network Segmentation

Cardiovascular function analysis is crucial for illness diagnosis, risk assessment, and therapy selection in clinical cardiology. Doctors may identify cardiac disorders such as right ventricular failure, hypertrophic cardiomyopathy, and dilated cardiomyopathy using a variety of imaging modalities that allow them to spot pathological alterations. The optimum course of therapy may be chosen more quickly thanks to accurate automation of the relevant duties. Artificial neural networks and deep learning are the foundation of generative adversarial networks (GANs), which are methods for creating synthetic pictures. The potential capacity of the GANs to solve problems has attracted interest in addition to their inherent flexibility and the adaptability of deep learning, on which they are founded. This survey aims to examine the significance of medical imaging in the study and diagnosis of cardiac disease. Demonstrate the widespread adoption of GAN Network approaches in the field of magnetic resonance imaging (MRI) medical image analysis; Explain the recent segmentation application of generative adversarial networks. GANs in cardiovascular imaging additionally Identify the hurdles to the effective application of the GAN Network to medical imaging tasks and highlight particular contributions that address or get around these problems

Data augmentation for design of concentric tube continuum robots by generative adversarial networks

AbstractConcentric tube continuum robots are a promising type of robot for various medical applications. Their application in neurosurgery poses challenging requirements for design and control that can be addressed by physics‐informed data‐based approaches. A prerequisite to data‐based modeling is an informative, rich data set. However, limited access to experimental data raises interest in partially or entirely synthetic data sets. In this contribution, we study the application of generative adversarial networks (GANs) for data augmentation in a data‐based design process of such robots. We propose a GAN framework suitable for curve‐fitting to generate synthetic trajectories of robots along with their corresponding control parameters. Our evaluation shows that the GANs can efficiently produce meaningful synthetic trajectories and control parameter pairs that show a good agreement with simulated trajectories.

Bi2O2Se-Based Bimode Noise Generator for the Application of Generative Adversarial Networks.

In the emerging technology, the generative aversive networks (GANs), randomness, and unpredictability of inputting noises are the keys to the uniqueness, diversity, robustness, and security of the generated images. Compared with deterministic software-based noise generation, hardware-based noise generation introduces physical entropy sources, such as electronic and photonic noises, to add unpredictability. In this study, bimode Bi2O2Se-based noise generators have been demonstrated for the application of GANs. Harnessing its ultrahigh carrier mobility, excellent air stability, marvelous optoelectronic performance, as well as the unique surface resistive switching effect and defect locations in the energy diagram, Bi2O2Se provides a good material platform to easily integrate with multiple device architectures for generating noises in different physical sources. The noise of the black current mode in a photodetector architecture and the random telegraph noise in a memristor mode were measured, characterized, compared, and analyzed. A method of Markov chain equipped with K-means clustering was carried out to calculate the discrete noise states and the transition probability matrix between them. To evaluate the generated properties of the GANs based on the hardware noise source, the inception score and Fréchet inception distance were evaluated.

A review of few-shot image recognition using semantic information

In recent years, the utilization of deep learning techniques has been employed in the field of image recognition with the aim of improving performance. However, deep learning demands a substantial amount of labeled data for model training, a process that is both expensive and time-consuming. In order to tackle this particular difficulty, the approach of few-shot learning (FSL) has emerged as a viable alternative. FSL, or Few-Shot Learning, is a computational approach that aims to replicate the cognitive processes observed in humans. By using a small set of examples and experiences, FSL enables the acquisition of new concepts. Research in the field of FSL has investigated many approaches to extracting the highest amount of information from limited data or making use of affordable and easily accessible sources of information. Researchers have been incorporating outside data into FSL techniques more frequently. This paper conducts an in-depth exploration of leveraging semantic information to enhance few-shot learning. By reviewing papers from the last five years in WOS, IEEE, and Science Direct (some papers in arXiv are also used), this study delves into the strategies employed to bridge the gap between visual and semantic information. The review extends to encompass zero-shot learning, which is considered a subcategory of FSL, enriching the analysis. Moreover, this paper identifies the potential of employing semantic information to enhance fine-grained few-shot (FGFS) learning. Techniques such as direct projection and the application of generative adversarial networks (GANs) emerge as promising avenues to accomplish this enhancement.

Novel applications of generative adversarial networks (GANs) in the analysis of ultrafast electron diffraction (UED) images.

Inferring transient molecular structural dynamics from diffraction data is an ambiguous task that often requires different approximation methods. In this paper, we present an attempt to tackle this problem using machine learning. Although most recent applications of machine learning for the analysis of diffraction images apply only a single neural network to an experimental dataset and train it on the task of prediction, our approach utilizes an additional generator network trained on both synthetic and experimental data. Our network converts experimental data into idealized diffraction patterns from which information is extracted via a convolutional neural network trained on synthetic data only. We validate this approach on ultrafast electron diffraction data of bismuth samples undergoing thermalization upon excitation via 800nm laser pulses. The network was able to predict transient temperatures with a deviation of less than 6% from analytically estimated values. Notably, this performance was achieved on a dataset of 408 images only. We believe that employing this network in experimental settings where high volumes of visual data are collected, such as beam lines, could provide insights into the structural dynamics of different samples.

Iterative application of generative adversarial networks for improved buried pipe detection from images obtained by ground‐penetrating radar

AbstractGround‐penetrating radar (GPR) is widely used to determine the location of buried pipes without excavation, and machine learning has been researched to automatically identify the location of buried pipes from the reflected wave images obtained by GPR. In object detection using machine learning, the accuracy of detection is affected by the quantity and quality of training data, so it is important to expand the training data to improve accuracy. This is especially true in the case of buried pipes that are located underground and whose existence cannot be easily confirmed. Therefore, this study developed a method for increasing training data using you only look once v5 (YOLOv5) and StyleGAN2‐ADA to automate the annotation process. Of particular importance is developing a framework for generating images by generative adversarial networks with an emphasis on images that are challenging to detect buried pipes in YOLOv5 and add them to a training dataset to repeat training recursively, which has greatly improved the detection accuracy. Specifically, F‐values of 0.915, 0.916, and 0.924 were achieved by automatically generating training images step by step from only 500, 1000, and 2000 training images, respectively. These values exceed the F‐value of 0.900, which is obtained from training by manually annotating 15,000 images, a much larger number. In addition, we applied the method to a road in Shizuoka Prefecture, Japan, and confirmed that the method can detect the location of buried pipes with high accuracy on a real road. This method can contribute to labor‐saving training data expansion, which is time‐consuming and costly in practice, and as a result, the method contributes to improving detection accuracy.

Challenges, Corresponding Solutions, and Applications of Generative Adversarial Networks

Generative adversarial networks (GANs) have become a popular deep learning framework in the field of artificial intelligence. Researchers have developed various types of GANs, such as Conditional GANs (CGANs), Mode Dropping GANs (MDGANs), and Wasserstein GANs (WGANs), which have been applied to many different fields. Despite the success of GANs in various tasks, there are still some challenges that need to be addressed. For example, model collapse, non-convergence, and diminished gradient could hinder the training of GANs. In this paper, the authors first introduce the basics of GANs before highlighting a few common problems associated with GANs and their corresponding solutions. For instance, they discuss techniques such as mini-batch discrimination and batch normalization that can help mitigate model collapse and diminish gradient problems. Additionally, they cover methods that can improve the convergence rate and stability of GANs, such as using alternative loss functions and incorporating regularization algorithms. Finally, this paper briefly introduces some recent applications of GANs in image generation, video prediction, and other areas. Despite the challenges that still exist, GANs have shown great promise in various fields, and with further research, GANs have the potential to become more robust and efficient models for generating high-quality synthetic data.