Generative Adversarial Networks Research Articles

Implementing generative adversarial networks for increasing performance of transmission fault classification

<p>An electrical power system is a network that facilitates the sourcing, transfer, and distribution of electrical energy. In the traditional power system, there are eleven types of faults that can occur in the system. This paper focuses on the classification of these faults over a stretch of 100 kilometres. The dataset used is synthetic and generated from a simulated model using MATLAB/Simulink software. Data augmentation is carried out during training to improve the accuracy of the classification. An indirect training approach through generative adversarial network (GAN) is used to classify these overhead transmission line faults. The random forest (RF) classification is used as the base learning model on the original dataset and it achieves accuracy of 84%. However, the base learner RF when used on GAN model generated augmented faulty data, it performs exceptionally well achieving 99% accuracy. One of the recent state-of-art methods is compared with this approach.</p>

Hydroacoustic optimization with using Noise-GAN

In recent years, noise pollution has a significant impact on marine organisms, hence requiring some restrictions and safety protocols. The primary objective of these restrictions is to minimize the noise generated by human-driven vehicles in aquatic environments. As a result, hydroacoustical studies are becoming increasingly integrated into design studies. The objective of this study is to provide an innovative approach to the design of hydrofoils, are considered a crucial component in the field of hydroacoustic engineering. The objective of this approach is to create a cutting-edge optimization tool through the integration of machine learning and hydroacoustic performance calculations. This article presents the development of a novel method called Noise-GAN, combines Generative Adversarial Networks (GAN) algorithms with hydroacoustic optimization capabilities. The GAN algorithm is used as a dimensionality reduction method in the optimization process, allowing for quick exploration of the design space and obtaining unique profile geometries during optimization. This method is used to generate optimal hydrofoil geometry for various conditions. The hydroacoustic and hydrodynamic performances of the generated geometry are analyzed to determine the applicability and performance of the method. The results are compared to two existing dimensionality reduction method to enhance comprehension of the performance. In order to gain insight into the performance of generated geometries compared to traditional geometries, the results are also compared to the performance data of the profiles available in the literature. This article also includes comparing the performance results of the NACA0009 profile, which is typically employed in hydrofoil research, with the optimal geometries to evaluate of the method and the performance of the generated geometries. In order to determine the real-life applicability of the hydrofoil geometries generated by the method, optimization studies were performed at the five different Angle of Attack (AoA) conditions within the range of 0o - 15o. In this way, the feasibility of the method at different AoA values was also examined. The results of all these comparisons demonstrate that the Noise-GAN method, which was developed to generate hydroacoustically optimized hydrofoil geometry, is highly effective in a variety of respects. The optimization tool developed in this study generates hydrodynamically efficient and unique hydrofoil profiles while optimizing for hydroacoustical performance. The results of this study are presented in this paper.

Decision-making dynamics in urban energy transition: A statistical analysis of policy frameworks, stakeholder collaboration and technological innovations

This research explores the intricate dynamics of decision-making in the context of urban energy transition, focusing on the interplay between policy frameworks, stakeholder collaboration, and technological innovations. The study makes three key contributions to the field. Firstly, it proposes an innovative approach to enhance market price prediction accuracy by integrating a Generative Adversarial Network (GAN) model. Leveraging the Adaptive Bat Algorithm (ABA) for GAN parameter optimization, the model exhibits improved precision in forecasting electricity prices, thereby providing stakeholders with more reliable insights. Secondly, the research introduces optimized model parameters for efficient energy decision-making, employing the ABA to dynamically adapt GAN parameters. This adaptive process enhances the overall performance of the model, facilitating better-informed decisions for stakeholders in the energy sector. Lastly, the integration of the ABA for GAN parameter optimization contributes to informed decision-making by generating more accurate market price values. This precision empowers policymakers, utilities, and investors with reliable insights, facilitating the development and implementation of strategies aligned with the goals of sustainable and efficient urban energy transition. The research underscores the significance of accurate market price predictions in shaping a resilient and sustainable energy landscape.

Maize Leaf Disease Image Enhancement Algorithm Using TFEGAN

Maize leaf diseases have a profound impact on maize crop yield. However, due to practical constraints, gathering a substantial number of maize disease images is time-consuming and labor-intensive. Furthermore, low-light conditions and noise interference in maize leaf disease images present significant challenges for image generation. Therefore, this paper proposes TFEGAN, a novel algorithm designed to generate maize leaf disease images. TFEGAN leverages a Two-Way Extraction Module (TWEM) to enhance the perceptual domain of the model and effectively reuse maize leaf disease features. The algorithm employs two well-trained Generative Adversarial Networks (GANs) to enhance low-light and noisy maize leaf disease images respectively. The low-light enhancement component utilizes a double discriminator and self-regularization to balance global and local enhancement while constraining the distance between the input image and the denoising component. Moreover, the denoising part employs a strong coordinated attention mechanism and an environmental encoder to learn noise regions and their surrounding structures, thus generating better local images and enabling more accurate evaluation by the discriminant network. The generators of both networks are encapsulated by two discriminators and combined with a bi-directional feature extraction structure to generate normal maize leaf disease images. Experimental evaluations demonstrate the effectiveness of TFEGAN in improving the quality of maize leaf disease images under low-light and noisy conditions, showcasing its potential for practical applications. Overall, this paper presents a promising solution to the challenges of low-light and noise interference in maize leaf disease images, utilizing bidirectional feature extraction and GANs.

A conditional generative adversarial networks and Yolov5 Darknet -based skin lesion localization and classification using independent component analysis model

Automated diagnosis systems significantly improve the early detection of skin cancer, thereby contributing to its successful treatment. Skin lesion segmentation represents a crucial yet challenging issue within the realm of computer-assisted dermoscopy image analysis. Numerous segmentation techniques reliant on convolutional neural networks falter in achieving precise lesion boundaries due to the diminishing spatial scale of feature maps as they traverse through network layers. Nonetheless, neural networks demand a substantial amount of data, which presents a notable obstacle in the medical field. More data, and possibly a pre-trained model tailored to medical images, are imperative. Researchers have resorted to extensive augmentation and preprocessing methods, as well as fine-tuning the network using pre-trained models on unrelated images, in order to address the challenge of lesion segmentation as the initial step in skin cancer analysis. This arises from the fact that datasets are limited in size and encompass a diverse array of images, varying in terms of lighting, color, scale, and markings. The proposed method addresses the issue of precisely locating and classifying skin lesions in dermatological images. The architecture of Conditional Generative Adversarial Networks (cGAN) enables the generation of high-resolution, contextually pertinent images, thereby enhancing the discriminative properties of skin lesions. YOLOv5 Darknet has an effective object detection mechanism that aids in precise lesion localization. Independent component analysis (ICA), a signal processing technique that identifies statistically separate components from diverse data sources, is employed to improve the model's robustness and interpretability further. This facilitates extracting latent features from the combined dermatological images and data collection, thereby enhancing the model's understanding of complex lesion characteristics. According to experimental results on an extensive dermatological data set, the proposed cGAN-YOLOv5 technique is effective. In pinpointing and classifying lesion locations, the hybrid model outperforms the competition. The synergistic integration of cGANs, YOLOv5 Darknet, and ICA demonstrates the effectiveness of multifaceted integration in enhancing medical image analysis. Using high-resolution feature blocks and a noise-suppressing attention method, cGAN-YOLOv5 is proposed. Therefore, the proposed strategy segments skin lesions effectively.

Detection of individual brain tau deposition in Alzheimer's disease based on latent feature-enhanced generative adversarial network

The conventional methods for interpreting tau PET imaging in Alzheimer's disease (AD), including visual assessment and semi-quantitative analysis of fixed hallmark regions, are insensitive to detect individual small lesions because of the spatiotemporal neuropathology's heterogeneity. In this study, we proposed a latent feature-enhanced generative adversarial network model for the automatic extraction of individual brain tau deposition regions. The latent feature-enhanced generative adversarial network we propose can learn the distribution characteristics of tau PET images of cognitively normal individuals and output the abnormal distribution regions of patients. This model was trained and validated using 1131 tau PET images from multiple centres (with distinct races, i.e., Caucasian and Mongoloid) with different tau PET ligands. The overall quality of synthetic imaging was evaluated using structural similarity (SSIM), peak signal to noise ratio (PSNR), and mean square error (MSE). The model was compared to the fixed templates method for diagnosing and predicting AD. The reconstructed images archived good quality, with SSIM = 0.967 ± 0.008, PSNR = 31.377 ± 3.633, and MSE = 0.0011 ± 0.0007 in the independent test set. The model showed higher classification accuracy (AUC = 0.843, 95 % CI = 0.796-0.890) and stronger correlation with clinical scales (r = 0.508, P < 0.0001). The model also achieved superior predictive performance in the survival analysis of cognitive decline, with a higher hazard ratio: 3.662, P < 0.001. The LFGAN4Tau model presents a promising new approach for more accurate detection of individualized tau deposition. Its robustness across tracers and races makes it a potentially reliable diagnostic tool for AD in practice.

Real-time diagnosis of intracerebral hemorrhage by generating dual-energy CT from single-energy CT

Real-time diagnosis of intracerebral hemorrhage after thrombectomy is crucial for follow-up treatment. However, this is difficult to achieve with standard single-energy CT (SECT) due to similar CT values of blood and contrast agents under a single energy spectrum. In contrast, dual-energy CT (DECT) scanners employ two different energy spectra, which allows for real-time differentiation between hemorrhage and contrast extravasation based on energy-related attenuation characteristics. Unfortunately, DECT scanners are not as widely used as SECT scanners due to their high costs. To address this dilemma, in this paper, we generate pseudo DECT images from a SECT image for real-time diagnosis of hemorrhage. More specifically, we propose a SECT-to-DECT Transformer-based Generative Adversarial Network (SDTGAN), which is a 3D transformer-based multi-task learning framework equipped with a shared attention mechanism. In this way, SDTGAN can be guided to focus more on high-density areas (crucial for hemorrhage diagnosis) during the generation. Meanwhile, the introduced multi-task learning strategy and the shared attention mechanism also enable SDTGAN to model dependencies between interconnected generation tasks, improving generation performance while significantly reducing model parameters and computational complexity. In the experiments, we approximate real SECT images using mixed 120kV images from DECT data to address the issue of not being able to obtain the true paired DECT and SECT data. Extensive experiments demonstrate that SDTGAN can generate DECT images better than state-of-the-art methods. The code of our implementation is available at https://github.com/jiang-cw/SDTGAN.

Deep Learning Synthesis of White-Blood From Dark-Blood Late Gadolinium Enhancement Cardiac Magnetic Resonance.

Dark-blood late gadolinium enhancement (DB-LGE) cardiac magnetic resonance has been proposed as an alternative to standard white-blood LGE (WB-LGE) imaging protocols to enhance scar-to-blood contrast without compromising scar-to-myocardium contrast. In practice, both DB and WB contrasts may have clinical utility, but acquiring both has the drawback of additional acquisition time. The aim of this study was to develop and evaluate a deep learning method to generate synthetic WB-LGE images from DB-LGE, allowing the assessment of both contrasts without additional scan time. DB-LGE and WB-LGE data from 215 patients were used to train 2 types of unpaired image-to-image translation deep learning models, cycle-consistent generative adversarial network (CycleGAN) and contrastive unpaired translation, with 5 different loss function hyperparameter settings each. Initially, the best hyperparameter setting was determined for each model type based on the Fréchet inception distance and the visual assessment of expert readers. Then, the CycleGAN and contrastive unpaired translation models with the optimal hyperparameters were directly compared. Finally, with the best model chosen, the quantification of scar based on the synthetic WB-LGE images was compared with the truly acquired WB-LGE. The CycleGAN architecture for unpaired image-to-image translation was found to provide the most realistic synthetic WB-LGE images from DB-LGE images. The results showed that it was difficult for visual readers to distinguish if an image was true or synthetic (55% correctly classified). In addition, scar burden quantification with the synthetic data was highly correlated with the analysis of the truly acquired images. Bland-Altman analysis found a mean bias in percentage scar burden between the quantification of the real WB and synthetic white-blood images of 0.44% with limits of agreement from -10.85% to 11.74%. The mean image quality of the real WB images (3.53/5) was scored higher than the synthetic white-blood images (3.03), P = 0.009. This study proposed a CycleGAN model to generate synthetic WB-LGE from DB-LGE images to allow assessment of both image contrasts without additional scan time. This work represents a clinically focused assessment of synthetic medical images generated by artificial intelligence, a topic with significant potential for a multitude of applications. However, further evaluation is warranted before clinical adoption.

Self-supervised graph representations with generative adversarial learning

Graph Neural Networks (GNNs) are powerful neural models for representation learning on graphs. In this work we study a self-supervised learning GNN method GraphSSGAN (Graph Self-Supervised Generative Adversarial Network) that learns generalized node representations using only unlabeled data. The core idea lies in learning representations that capture both the local information of the links and the global information of the plausibility of the subgraph samples. Specifically, node features of the original graph are first encoded into latent representations by optimizing a link prediction objective. Then the generator converts noise vectors to node representations in the latent space and predicts the link probabilities from the generated node representations. The discriminator leverages the graph convolutional network (GCN) architecture to produce permutation-invariant graph-level embeddings, and the intermediate node representations are used by simple classifiers in the downstream tasks. In addition, we introduce several technical tricks including Gumbel-Top-k trick, Gumbel-Softmax trick and mini-batch training via subgraph sampling to improve the training process. Through extensive experiments on node classification and link prediction tasks, we demonstrate the effectiveness of the proposed model and the contribution of GAN framework on graph representation learning.

Data-driven distributionally robust day-ahead dispatch for active distribution networks based on improved conditional generative adversarial network

With the increasing penetration of renewable energy generation (REG), strong uncertainty poses a significant challenge to the rationality of day-ahead dispatch plans in distribution systems. We propose a data-driven, distributionally robust day-ahead dispatch method for active distribution networks (ADNs) based on an improved conditional generative adversarial network (CGAN). First, an improved CWGAN-GP model is constructed based on three-dimensional convolution (Conv3D) and the Wasserstein distance to obtain the day-ahead REG scenarios, considering the correlation features of photovoltaic (PV) and wind turbines (WTs), which reduces the conservatism of scenario generation. Accordingly, the Gaussian mixture model (GMM) clustering method based on moment information was used to construct an ambiguity set. Moreover, a two-stage distributionally robust day-ahead dispatch model for an ADN is established, which realizes the fusion of the data-driven method and distributionally robust optimization (DRO) mechanism model. Finally, the validity of the data-driven DRO (DDRO) method is verified through a case analysis.

A novel RCACycleGAN model is proposed for the high-precision reconstruction of sparse TFM images

The sparse total focusing method (TFM) has been shown to enhance the computational efficacy of ultrasound imaging but the image quality of ultrasound regrettably deteriorates with an increase in the sparsity rate of array elements. Deep learning has made remarkable advancements in image processing and cycle-consistent generative adversarial networks (CycleGANs) have been extensively employed to reconstruct diverse image categories. However, due to the incomplete extraction of image feature information by the generator and discriminator in a CycleGAN, high-quality sparse TFM images cannot be directly reconstructed using CycleGANs. There is also a risk of losing crucial feature information related to minor defects. As a result, this paper modifies the generator and discriminator in the CycleGAN to construct a new relativistic discriminator and coordinate attention CycleGAN (RCACycleGAN) model, which enables high-precision reconstruction of sparse TFM images. The addition of the coordinate attention module to the CycleGAN enhances the defective feature representation by fully considering the channel and spatial correlation between regions and using the fusion of spatially perceived feature maps in different directions. It solves the problem of easy loss of defective key feature information. The relativistic discriminator replaces the PatchGAN discriminator in the CycleGAN and evaluates the quality of both real and sparse TFM reconstructed images to ensure a relative image quality evaluation. This process solves the problem of unstable image quality of the sparse TFM reconstructed image. Experimental results demonstrate that RCACycleGAN can stably reconstruct sparse TFM images even in small sample dataset scenarios. The proposed network model reconstructs images with better accuracy, including in terms of structural similarity, defect roundness and area, and has a shorter training time than several existing network models.

CVEGAN: A perceptually-inspired GAN for Compressed Video Enhancement

We propose a new Generative Adversarial Network for Compressed Video frame quality Enhancement (CVEGAN). The CVEGAN generator benefits from the use of a novel Mul2Res block (with multiple levels of residual learning branches), an enhanced residual non-local block (ERNB) and an enhanced convolutional block attention module (ECBAM). The ERNB has also been employed in the discriminator to improve the representational capability. The training strategy has also been re-designed specifically for video compression applications, to employ a relativistic sphere GAN (ReSphereGAN) training methodology together with new perceptual loss functions. The proposed network has been fully evaluated in the context of two typical video compression enhancement tools: post-processing (PP) and spatial resolution adaptation (SRA). CVEGAN has been fully integrated into the MPEG HEVC and VVC video coding test models (HM 16.20 and VTM 7.0) and experimental results demonstrate significant coding gains (up to 28% for PP and 38% for SRA compared to the anchor) over existing state-of-the-art architectures for both coding tools across multiple datasets based on the HM 16.20. The respective gains for VTM 7.0 are up to 8.0% for PP and up to 20.3% for SRA.

Conditional generative model with skip-connection structure for low-light image enhancement

Vision-guided coal mine robots often encounter challenges in low-light environments, capturing images marked by poor visibility and significant loss of detail, which complicates the advancement of smart mine safety production. Despite various advancements, achieving high visibility and detailed images through robotic vision sensors remains a formidable task. This paper introduces a conditional generative model incorporating a skip-connection structure to address these issues. We present a novel approach utilizing conditional Generative Adversarial Networks (GNAs) aimed at improving the visibility of image content. This involves an encoder-decoder framework with skip-connections functioning as the generator, and the discriminator, both tailored to enhance image detail. In addition, we implement loss functions integrated with boundary equilibrium constraints to counter the issue of model collapse in conditional GANs, thereby enhancing training stability and image fidelity. Our experimental findings demonstrate that our model delivers competitive results against other state-of-the-art low-light image enhancement models, alongside a standardized training regimen that ensures rapid and consistent convergence.

MOS-GAN: A U-Net++ based GAN for multi-organ segmentation

Radiotherapy, a standard and common form of cancer treatment, is used in about half of all cancer cases. It is critical to segment organs at risk (OARs) accurately and promptly for radiation treatment planning to be efficient and high-quality. In this paper, an adversarial training strategy is used in our deep learning network. This model is called the Multiple Organ Segmentation Generative Adversarial Network (MOS-GAN). The U-Net++ is our generator to create a multi-organ segmentation image by learning the end-to-end mapping from CT images to OARs segmentation. A convolutional neural network (CNN) acts as the discriminator to identify the difference between the manual contour and the segmented images created by the generator. A 2.5D method is employed by us to extract the spatial information among slices. We applied the proposed method to segment the five organs from the 2017 AAPM segmentation challenge dataset. Segmentation of the bilateral lungs, heart, and spinal cord were successfully achieved, as well as the outline of the esophagus. In terms of dice similarity coefficients, the aforementioned five OARs have average values of 0.97, 0.97, 0.92, 0.91 and 0.77, respectively.

A novel approach for melanoma detection utilizing GAN synthesis and vision transformer

Background and objective:Melanoma, a malignant form of skin cancer, is a critical health concern worldwide. Early and accurate detection plays a pivotal role in improving patient’s conditions. Current diagnosis of skin cancer largely relies on visual inspections such as dermoscopy examinations, clinical screening and histopathological examinations. However, these approaches are characterized by low efficiency, high costs, and a lack of guaranteed accuracy. Consequently, deep learning based techniques have emerged in the field of melanoma detection, successfully aiding in improving the accuracy of diagnosis. However, the high similarity between benign and malignant melanomas, combined with the class imbalance issue in skin lesion datasets, present a significant challenge in further improving the diagnosis accuracy. We propose a two-stage framework for melanoma detection to address these issues. Methods:In the first stage, we use Style Generative Adversarial Networks with Adaptive discriminator augmentation synthesis to generate realistic and diverse melanoma images, which are then combined with the original dataset to create an augmented dataset. In the second stage, we utilize a vision Transformer of BatchFormer to extract features and detect melanoma or non-melanoma skin lesions on the augmented dataset obtained in the previous step, specifically, we employed a dual-branch training strategy in this process. Results:Our experimental results on the ISIC2020 dataset demonstrate the effectiveness of the proposed approach, showing a significant improvement in melanoma detection. The method achieved an accuracy of 98.43%, an AUC value of 98.63%, and an F1 value of 99.01%, surpassing some existing methods. Conclusion:The method is feasible, efficient, and achieves early melanoma screening. It significantly enhances detection accuracy and can assist physicians in diagnosis to a great extent.

Vein Pattern Visualisation for Biometric Identification with cGAN on a New Zealand Dataset

Forensic identification using vein patterns in standard colour images presents significant challenges due to their low visibility. Recent efforts have employed various computational techniques, including artificial neural networks and optical vein disclosure, to enhance vein pattern detection. However, these methods still face limitations in reliability when compared to Near-Infrared (NIR) reference images. One of the biggest challenges of the studies is the limited number of available datasets that have synchronised colour and NIR images from body limbs. This paper introduces a new dataset comprising 602 pairs of synchronised NIR and RGB forearm images from a diverse population, ethically approved and collected in Auckland, New Zealand. Using this dataset, we also propose a conditional Generative Adversarial Networks (cGANs) model to translate RGB images into their NIR equivalents. Our evaluations focus on matching accuracy, vein length measurements, and contrast quality, demonstrating that the translated vein patterns closely resemble their NIR counterparts. This advancement offers promising implications for forensic identification techniques.

TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction

Inter-frame motion in dynamic cardiac positron emission tomography (PET) using rubidium-82 (82Rb) myocardial perfusion imaging impacts myocardial blood flow (MBF) quantification and the diagnosis accuracy of coronary artery diseases. However, the high cross-frame distribution variation due to rapid tracer kinetics poses a considerable challenge for inter-frame motion correction, especially for early frames where intensity-based image registration techniques often fail. To address this issue, we propose a novel method called Temporally and Anatomically Informed Generative Adversarial Network (TAI-GAN) that utilizes an all-to-one mapping to convert early frames into those with tracer distribution similar to the last reference frame. The TAI-GAN consists of a feature-wise linear modulation layer that encodes channel-wise parameters generated from temporal information and rough cardiac segmentation masks with local shifts that serve as anatomical information. Our proposed method was evaluated on a clinical 82Rb PET dataset, and the results show that our TAI-GAN can produce converted early frames with high image quality, comparable to the real reference frames. After TAI-GAN conversion, the motion estimation accuracy and subsequent myocardial blood flow (MBF) quantification with both conventional and deep learning-based motion correction methods were improved compared to using the original frames. The code is available at https://github.com/gxq1998/TAI-GAN.

Automatic detection of potholes using VGG-16 pre-trained network and Convolutional Neural Network

A self-driving car is necessary to implement traffic intelligence because it can vastly enhance both the safety of driving and the comfort of the driver by adjusting to the circumstances of the road ahead. Road hazards such as potholes can be a big challenge for autonomous vehicles, increasing the risk of crashes and vehicle damage. Real-time identification of road potholes is required to solve this issue. To this end, various approaches have been tried, including notifying the appropriate authorities, utilizing vibration-based sensors, and engaging in three-dimensional laser imaging. Unfortunately, these approaches have several drawbacks, such as large initial expenditures and the possibility of being discovered. Transfer learning is considered a potential answer to the pressing necessity of automating the process of pothole identification. A Convolutional Neural Network (CNN) is constructed to categorize potholes effectively using the VGG-16 pre-trained model as a transfer learning model throughout the training process. A Super-Resolution Generative Adversarial Network (SRGAN) is suggested to enhance the image's overall quality. Experiments conducted with the suggested approach of classifying road potholes revealed a high accuracy rate of 97.3%, and its effectiveness was tested using various criteria. The developed transfer learning technique obtained the best accuracy rate compared to many other deep learning algorithms.

Integrating Pix2Pix and Computational Fluid Dynamics for Enhanced Indoor Airflow Prediction: A Case Study with Wing-Walls

Computational Fluid Dynamics (CFD) is often used for analyzing natural ventilation but is limited in early design stages due to its high computational demands and the need for detailed inputs. To overcome such limitations of CFD simulation, this paper proposes a method using a conditional Generative Adversarial Network (cGAN) based image-to-image translation (Pix2Pix) to predict the indoor airflow condition. Compared to other traditional machine learning models that predict averaged values like indoor air velocity, Pix2Pix can predict contour plot of indoor airflow for the given building floor plan image, which provides more intuitive feedback to designers. The proposed method was tested to understand indoor air movement caused by wing-walls attached to the windows. The model was trained using 1,153 pairs of floor plan images, incorporating variations in window widths, wing-wall depth, wing-wall angle, wind speed, and wind direction. It achieved a prediction accuracy of 94% and produced results in less than a second. Moreover, the model showed relatively better performance with the changes in windows and wing-walls properties rather than wind properties. This high performance indicates that Pix2Pix can serve as an efficient proxy model of conventional CFD simulations, helping designers optimize ventilation in building designs at an early stage without the need for complex inputs.

Comprehensive Overview of GAN Technology: Architecture, Training, and Applications

Abstract: Generative Adversarial Networks (GANs) is a groundbreaking artificial intelligence technology that transforms generative modeling through the implementation of a novel adversarial training framework. GANs are made up of two neural networks, the generator and the discriminator, which compete in a minimax game in order to generate counterfeit samples of data and distinguish between real and fake data. This adversarial training method results in the creation of highly proficient generative models capable of producing data that is identical to real-world samples. GANs have been shown to have significant outcomes in a variety of programs, consisting of synthesizing images, style transfer, medical visualization, and natural language processing. However, GANs encounter difficulties with problems like mode collapse and operational irregularities. Presently, research is aimed at tackling these problems and strengthening GAN frameworks and training approaches. GANs’ adaptability and perspective contribute to an intriguing technology for a wide range of sectors and artistic activities, with consequences for artificial intelligence growth and generative modeling breakthroughs. GANs incorporate the potential of competitive training with their capacity to generate extremely realistic data in a variety of domains. We will begin by looking at the fundamental concepts, and underlying principles that reinforce GANs, and their general architecture, investigate their different possible uses, and discuss the obstacles and potential developments in this instantly transforming field.