Convolutional Generative Adversarial Networks Research Articles

Improving the Parameterization of Complex Subsurface Flow Properties With Style‐Based Generative Adversarial Network (StyleGAN)

AbstractRepresenting and preserving complex (non‐Gaussian) spatial patterns in aquifer flow properties during model calibration are challenging. Conventional parameterization methods that rely on linear/Gaussian assumptions are not suitable for representation of property maps with more complex spatial patterns. Deep learning techniques, such as Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN), have recently been proposed to address this difficulty by learning complex spatial patterns from prior training images and synthesizing similar realizations using low‐dimensional latent variables with Gaussian distributions. The resulting Gaussian latent variables lend themselves to calibration with the ensemble Kalman filter‐based updating schemes that are suitable for parameters with Gaussian distribution. Despite their superior performance in generating complex spatial patterns, these generative models may not provide desirable properties that are needed for parameterization of model calibration problems, including robustness, smoothness in the latent domain, and reconstruction fidelity. This paper introduces the second generation of style‐based Generative Adversarial Networks (StyleGAN) for parameterization of complex subsurface flow properties and compares its model calibration properties and performance with those of the convolutional VAE and GAN architectures. Numerical experiments involving model calibration with the Ensemble Smoother with Multiple Data Assimilation (ES‐MDA) in single‐phase and two‐phase fluid flow examples are used to assess the capabilities and limitations of these methods. The results show that parameterization with StyleGANs provides superior performance in terms of reconstruction fidelity and flexibility, underscoring its potential for improving the representation and reconstruction of complex spatial patterns in subsurface flow model calibration problems.

Comparison Between Gans and Diffusion Models in the Generation of Synthetic Images for Enhancing Tree Species Recognition

Objective: This study investigates the application of Generative Adversarial Networks (GANs) and Diffusion Models in data augmentation to improve the classification of tree species images, which is essential for sustainable forest management. Theoretical Framework: Fundamental concepts of machine learning, generative and classifier networks, as well as data augmentation techniques through synthetic image generation, are presented, establishing a solid foundation for the research. Method: The research utilized 2,178 images of cross-sections of wood from 18 species, applying Deep Convolutional GAN (DCGAN) and U-Net with diffusion on a rare species, evaluated using FID and IS metrics. The generated images were used to train and validate classification models, assessed by F1-score. Results and Discussion: The results revealed that Diffusion Models generated more realistic images and performed better in the classification of the rare species. The discussion contextualizes these results in light of the theoretical framework, exploring their implications for environmental management. Limitations, such as the impact of indiscriminate addition of synthetic data, are also addressed. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of forest management and environmental management. These implications may cover areas such as sustainable innovation and biodiversity conservation. Originality/Value: This study contributes to the literature by demonstrating the potential of Diffusion Models to outperform GANs in generating synthetic images for sustainable forest management purposes. The relevance and value of this research are evidenced by its practical application in promoting innovative and sustainable practices in environmental management.

Performance evaluation of generative adversarial networks for anime face synthesis using deep learning approaches

Anime characters have transcended their traditional animation role, permeating entertainment, video games, and social media. Online platforms, including prominent blogs and forums, are filled with references and adaptations of these characters. This digital ubiquity has spurred creators to develop innovative storytelling methods through the internet, fostering new avenues for anime character creation. Notably, advanced systems like Generative Adversarial Networks (GANs) have emerged as powerful tools for this purpose. This research investigates the capabilities of various GAN variants in generating anime faces. The Fréchet Inception Distance (FID) serves as a key metric for performance comparison. The analysis focuses on Deep Convolutional GAN (DCGAN), Progressively Growing GAN (ProGAN), and Style GAN2. Additionally, the performance metrics of the DCGAN model are examined. The findings show demonstrate significant differences in FID scores across these GANs. Notably, Style GAN2 exhibits superior performance in generating anime faces, achieving a significantly lower FID score (31) compared to DCGAN (625) and ProGAN (218.3). This research underscores Style GAN2's superior effectiveness in creating anime characters within the digital landscape.

Accelerating optimal scheduling prediction in power system: A multi-faceted GAN-assisted prediction framework

This study introduces a comprehensive framework aimed at enhancing power system optimality through a two-stage optimization process and the development of a deep-based model for optimal scheduling prediction (OSP). Initially, a Bidirectional Long Short-term Memory (Bi-LSTM) architecture is employed to accurately forecast wind power in the first stage. Subsequently, a convolutional Generative Adversarial Network (GAN) model utilizes these predicted wind power values to generate synthetic scenarios. These scenarios, based on the preceding 10 days’ wind power predictions, serve as inputs for the subsequent power system optimization stage. To streamline computational efficiency, the power system optimization is conducted via a two-stage model. The outputs from this process, alongside other pertinent parameters, are utilized to train the proposed deep-based OSP model. The efficacy of the proposed model in rapidly and reliably predicting optimal scheduling is evaluated using the 118-bus power system. Results indicate that the innovative approach demonstrates exceptional speed and precision in determining optimal scheduling for the power system. Specifically, the proposed OSP model accurately forecasts optimal dispatch for ten days ahead in a mere 0.38 s, with an error rate below 0.001. Furthermore, the model exhibits a 92 % correlation in predicting optimal dispatched wind power. Sensitivity analysis highlights that optimizing the arrangement of the proposed deep-based model using an automatic hyperparameter optimization software framework (OPTUNA) can significantly enhance performance accuracy, potentially by up to 24 %.

Using machine learning for chemical-free histological tissue staining

ABSTRACT Hematoxylin and eosin staining can be hazardous, expensive, and prone to error and variability. To circumvent these issues, artificial intelligence/machine learning models such as generative adversarial networks (GANs), are being used to ‘virtually’ stain unstained tissue images indistinguishable from chemically stained tissue. Frameworks such as deep convolutional GANs (DCGAN) and conditional GANs (CGANs) have successfully generated highly reproducible ‘stained’ images. However, their utility may be limited by requiring registered, paired images which can be difficult to obtain. To avoid these dataset requirements, we attempted to use an unsupervised CycleGAN pix2pix model(5,6) to turn unpaired, unstained bright-field images into pathologist-approved digitally ‘stained’ images. Using formalin-fixed-paraffin-embedded liver samples, 5µm section images (20x) were obtained before and after staining to create “stained” an “unstained” datasets. Model implementation was conducted using Ubuntu 20.04.4 LTS, 32 GB RAM, Intel Core i7-9750 CPU @2.6 GHz, Nvidia GeForce RTX 2070 Mobile, Python 3.7.11 and Tensorflow 2.9.1. The CycleGAN framework utilized a u-net-based generator and discriminator from pix2pix, a CGAN. The CycleGAN used a modified loss function, cycle consistent loss that assumed unpaired images, so loss was measured twice. To our knowledge, this is the first documented application of this architecture using unpaired bright-field images. Results and suggested improvements are discussed.

Garbage In [formula omitted] Garbage Out: Exploring GAN Resilience to Image Training Set Degradations

Generative Adversarial Networks (GANs) have received immense attention in recent years due to their ability to capture complex, high-dimensional data distributions without the need for extensive labeling. Since their conception in 2014, a wide array of GAN variants have been proposed featuring alternative architectures, optimizers, and loss functions with the goal of improving performance and training stability. This manuscript focuses on quantifying the resilience of a GAN architecture to specific modes of image degradation. We conduct systematic experimentation to empirically determine the effects of 10 fundamental image degradation modes, applied to the training image dataset, on the Fréchet inception distance (FID) of images generated by a conditional deep convolutional GAN (cDCGAN). We find that at the α=0.05 level, brightening, darkening, and blurring are statistically significantly more detrimental to the resulting GAN image quality than removing the degraded data completely, while other degradations are typically safe to keep in training datasets. Additionally, we find that in the case of randomized partial occlusion, the FID of the resulting GAN images approaches that of the degraded training set for increasing levels of occlusion, with the surprising result that GAN FID performance is equal to that of the training set at 75% degradation.

Investigation of generative capacity related to DCGANs across varied discriminator architectures and parameter counts: A comparative study

Generating lifelike images through generative models poses a significant challenge, where Generative Adversarial Networks (GANs), particularly Deep Convolutional GANs (DCGANs), are commonly employed for image synthesis. This study focuses on altering the DCGAN discriminators structure and parameter count, investigating their effects on the characteristics of the resulting generated images. Assessment of these models is carried out using the Frchet Inception Distance (FID) score, a metric that gauges the quality of generated image samples. The research specifically involves substituting some convolutional layers with fully-connected layers, and the ensuing outcomes are thoroughly compared to discern the impact of these structural changes. Furthermore, dropout was used to study the number of the parameters influence. This study compared the FID score of the models when the probability is 0, 0.2, 0.4, 0.6 and 0.8. Experimental results showed that the DCGAN with the fully-connected layers generated ability was stronger than the original one. Besides, when the probability of the dropout is 0.6, the images generated was the most realistic. Finally, the paper explained the possible reasons for the difference and proposed a better generative model based on DCGAN.

Colorectal cancer prediction via histopathology segmentation using DC-GAN and VAE-GAN

Colorectal cancer ranks as the third most common form of cancer in the United States. The Centres of Disease Control and Prevention report that males and individuals assigned male at birth (AMAB) have a slightly higher incidence of colon cancer than females and those assigned female at birth (AFAB) Black humans are more likely than other ethnic groups or races to develop colon cancer. Early detection of suspicious tissues can improve a person's life for 3-4 years. In this project, we use the EBHI-seg dataset. This study explores a technique called Generative Adversarial Networks (GAN) that can be utilized for data augmentation colorectal cancer histopathology Image Segmentation. Specifically, we compare the effectiveness of two GAN models, namely the deep convolutional GAN (DC-GAN) and the Variational autoencoder GAN (VAE-GAN), in generating realistic synthetic images for training a neural network model for cancer prediction. Our findings suggest that DC-GAN outperforms VAE-GAN in generating high-quality synthetic images and improving the neural network model. These results highlight the possibility of GAN-based data augmentation to enhance machine learning models’ performance in medical image analysis tasks. The result shows DC-GAN outperformed VAE-GAN.

SSDNet: A SEMISUPERVISED DEEP GENERATIVE ADVERSARIAL NETWORK FOR ELECTROENCEPHALOGRAM-BASED EMOTION RECOGNITION

This research introduces a novel method for emotion recognition using Electroencephalography (EEG) signals, leveraging advancements in emotion computing and EEG signal processing. The proposed method utilizes a semisupervised deep convolutional generative adversarial network (SSDNet) as the central model. The model fully integrates the feature extraction methods of the generative adversarial network (GAN), deep convolutional GAN (DCGAN), spectrally normalized GAN (SSGAN) and the encoder, maximizing the advantages of each method to construct a more accurate emotion classification model. In our study, we introduce a flow-form-consistent merging pattern, which successfully addresses mismatches between the data by fusing the EEG data with the features. Implementing this merging pattern not only enhances the uniformity of the input but also decreases the computational load on the network, resulting in a more efficient model. By conducting experiments on the DEAP and SEED datasets, we evaluate the SSDNet model proposed in this paper in detail. The experimental results show that the accuracy of the proposed algorithm is improved by 6.4% and 8.3% on the DEAP and SEED datasets, respectively, compared to the traditional GAN. This significant improvement in performance validates the effectiveness and feasibility of the SSDNet model. The research contributions of this paper are threefold. First, we propose and implement an SSDNet model that integrates multiple feature extraction methods, providing a more accurate and comprehensive solution for emotion recognition tasks. Second, by introducing a flow-form-consistent merging pattern, we successfully address the problem of interdata mismatches and improve the generalization performance of the model. Finally, we experimentally demonstrate that the method in this paper achieves a significant improvement in accuracy over the traditional GANs on the DEAP and SEED datasets, providing an innovative solution in the field of EEG-based emotion recognition.

U-shaped convolutional transformer GAN with multi-resolution consistency loss for restoring brain functional time-series and dementia diagnosis.

The blood oxygen level-dependent (BOLD) signal derived from functional neuroimaging is commonly used in brain network analysis and dementia diagnosis. Missing the BOLD signal may lead to bad performance and misinterpretation of findings when analyzing neurological disease. Few studies have focused on the restoration of brain functional time-series data. In this paper, a novel U-shaped convolutional transformer GAN (UCT-GAN) model is proposed to restore the missing brain functional time-series data. The proposed model leverages the power of generative adversarial networks (GANs) while incorporating a U-shaped architecture to effectively capture hierarchical features in the restoration process. Besides, the multi-level temporal-correlated attention and the convolutional sampling in the transformer-based generator are devised to capture the global and local temporal features for the missing time series and associate their long-range relationship with the other brain regions. Furthermore, by introducing multi-resolution consistency loss, the proposed model can promote the learning of diverse temporal patterns and maintain consistency across different temporal resolutions, thus effectively restoring complex brain functional dynamics. We theoretically tested our model on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and our experiments demonstrate that the proposed model outperforms existing methods in terms of both quantitative metrics and qualitative assessments. The model's ability to preserve the underlying topological structure of the brain functional networks during restoration is a particularly notable achievement. Overall, the proposed model offers a promising solution for restoring brain functional time-series and contributes to the advancement of neuroscience research by providing enhanced tools for disease analysis and interpretation.

Improved bioimpedance spectroscopy tissue classification through data augmentation from generative adversarial networks.

Bioimpedance spectroscopy is a tissue classification technique with many clinical applications. Similarly to other data-driven methods, it requires large amounts of data to accurately distinguish similar classes of tissue. Classifiers trained on small datasets typically suffer from over-fitting and lack the ability to generalise to previously unseen data. However, a large in or ex vivo spectral database is difficult to attain. Data collection is usually limited to studies that occur infrequently, and publicly available data is often not available. A solution to this problem is to artificially increase the training dataset by creating modified, yet accurate, copies of the original dataset. The most common techniques in spectral classification are to add noise to copies of the original data, over-sample it, or randomly interpolate pairs of the original data. However, simply perturbing or interpolating the data does not guarantee that the new dataset captures the key features of the original data needed for accurate classification. This study proposes a novel way to augment bioimpedance spectral data. It uses generative adversarial networks (GAN)-a model in which two neural networks (NN) compete with each other: while one NN artificially manufactures data that could be mistaken for real data, the role of the second NN is to identify which data it receives has been artificially created. The first NN then interactively adapts its output until the second NN can no longer flag artificially created data. The result is a new dataset that truly represents the features of the original data. In this study, three GAN architectures are used, i.e., the vanilla GAN, the deep convolutional GAN, and the Wasserstein GAN. Then, the generated data is used to train five classification methods, and their results are compared to a baseline that only uses the original data. The results from a dataset of 13 different tissue classes show that the deep convolutional GAN is most statistically similar to the original data and improves classification accuracy by 15% when compared to the same model trained only on the original data. The Wasserstein-GAN architecture also provides significant improvements of up to 24% better accuracy.

Clinical Utility of Breast Ultrasound Images Synthesized by a Generative Adversarial Network.

This study compares the clinical properties of original breast ultrasound images and those synthesized by a generative adversarial network (GAN) to assess the clinical usefulness of GAN-synthesized images. We retrospectively collected approximately 200 breast ultrasound images for each of five representative histological tissue types (cyst, fibroadenoma, scirrhous, solid, and tubule-forming invasive ductal carcinomas) as training images. A deep convolutional GAN (DCGAN) image-generation model synthesized images of the five histological types. Two diagnostic radiologists (reader 1 with 13 years of experience and reader 2 with 7 years of experience) were given a reading test consisting of 50 synthesized and 50 original images (≥1-month interval between sets) to assign the perceived histological tissue type. The percentages of correct diagnoses were calculated, and the reader agreement was assessed using the kappa coefficient. The synthetic and original images were indistinguishable. The correct diagnostic rates from the synthetic images for readers 1 and 2 were 86.0% and 78.0% and from the original images were 88.0% and 78.0%, respectively. The kappa values were 0.625 and 0.650 for the synthetic and original images, respectively. The diagnoses made from the DCGAN synthetic images and original images were similar. The DCGAN-synthesized images closely resemble the original ultrasound images in clinical characteristics, suggesting their potential utility in clinical education and training, particularly for enhancing diagnostic skills in breast ultrasound imaging.

SAM-GAN: An improved DCGAN for rice seed viability determination using near-infrared hyperspectral imaging

Viability is a significant indicator of rice seeds, affecting rice yield and quality. Existing viability determination methods cannot meet the requirements of rapidity, non-destructive and accuracy. In this study, near-infrared hyperspectral imaging was used to detect the viability of natural aging seeds. Generative Adversarial Network (GAN) is the main means of coping with Few-shot learning. Considering that natural aging seed samples were difficult to obtain and the number was scarce, this study used Spectral Angle Mapper Generative Adversarial Network (SAM-GAN) to generate rice seed spectral data based on the spectra of obtained natural aging seeds to solve the problem of sample scarcity. SAM-GAN is based on Deep Convolution GAN (DCGAN), introduced by SAM. SAM-GAN was compared with Wasserstein Generative Adversarial Nets with Gradient Penalty (WGAN-GP) and DCGAN, and the Convolutional Neural Network (CNN) model was established by three modeling methods: real data modeling, fake data modeling and mixed modeling of real data and fake data. The experimental results show that the accuracy of the CNN model established by mixing real data with fake data generated by SAM-GAN reaches nearly 100%. This study provides an effective method for rapid, non-destructive and accurate determination of rice seed viability with a limited sample number.

On the convergence acceleration of vanilla Generative Adversarial Network

Generative Adversarial Network (GAN) is very successful and widely used in both academic and industrial fields. It is inspired by game theory where two models, a generator and a critic, compete with each other, learn from each other, and make each other stronger and stronger. Most improvements focused on stability and there are already many different models such as deep convolutional GAN (DCGAN) and Wassertein GAN (WGAN) that have succeeded. But the speed of the convergence during training process is also an essential ability that needs to be enhanced. In this paper, a decreasing noise is added to the MNIST data and these new data are delivered to the discriminator of the vanilla GAN to accelerate the training process. The result shows that this change could accelerate the convergence and not affect the results’ quality a lot. And this improvement should be general enough to apply to the other models.

Deep convolutional and conditional neural networks for large-scale genomic data generation.

Applications of generative models for genomic data have gained significant momentum in the past few years, with scopes ranging from data characterization to generation of genomic segments and functional sequences. In our previous study, we demonstrated that generative adversarial networks (GANs) and restricted Boltzmann machines (RBMs) can be used to create novel high-quality artificial genomes (AGs) which can preserve the complex characteristics of real genomes such as population structure, linkage disequilibrium and selection signals. However, a major drawback of these models is scalability, since the large feature space of genome-wide data increases computational complexity vastly. To address this issue, we implemented a novel convolutional Wasserstein GAN (WGAN) model along with a novel conditional RBM (CRBM) framework for generating AGs with high SNP number. These networks implicitly learn the varying landscape of haplotypic structure in order to capture complex correlation patterns along the genome and generate a wide diversity of plausible haplotypes. We performed comparative analyses to assess both the quality of these generated haplotypes and the amount of possible privacy leakage from the training data. As the importance of genetic privacy becomes more prevalent, the need for effective privacy protection measures for genomic data increases. We used generative neural networks to create large artificial genome segments which possess many characteristics of real genomes without substantial privacy leakage from the training dataset. In the near future, with further improvements in haplotype quality and privacy preservation, large-scale artificial genome databases can be assembled to provide easily accessible surrogates of real databases, allowing researchers to conduct studies with diverse genomic data within a safe ethical framework in terms of donor privacy.

Comparison of Deep Convolutional GAN and Progressive GAN for facial image generation

Generative Adversarial Networks (GANs) have generated realistic and diverse facial images with promising results. This work demonstrates a technique for creating facial pictures using GANs and evaluates the effectiveness of several GAN designs and training approaches. The CelebA dataset is leveraged for training and evaluating the GAN model, and employ a variety of evaluation metrics, such as the Structural Similarity Index (SSIM) to assess the quality and diversity of the generated images. Progressive GAN outperforms Deep Convolutional GAN in terms of image quality and diversity, and conditional GAN training is more effective than standard GAN training for generating facial images with specific attributes. The combination of Progressive GAN and conditional GAN training produces facial images of the utmost quality and diversity. The findings contribute to a broader comprehension of the use of GANs for generating facial images and have ramifications for a variety of applications, from facial recognition to virtual reality.

Breast Ultrasound Images Augmentation and Segmentation Using GAN with Identity Block and Modified U-Net 3.

One of the most prevalent diseases affecting women in recent years is breast cancer. Early breast cancer detection can help in the treatment, lower the infection risk, and worsen the results. This paper presents a hybrid approach for augmentation and segmenting breast cancer. The framework contains two main stages: augmentation and segmentation of ultrasound images. The augmentation of the ultrasounds is applied using generative adversarial networks (GAN) with nonlinear identity block, label smoothing, and a new loss function. The segmentation of the ultrasounds applied a modified U-Net 3+. The hybrid approach achieves efficient results in the segmentation and augmentation steps compared with the other available methods for the same task. The modified version of the GAN with the nonlinear identity block overcomes different types of modified GAN in the ultrasound augmentation process, such as speckle GAN, UltraGAN, and deep convolutional GAN. The modified U-Net 3+ also overcomes the different architectures of U-Nets in the segmentation process. The GAN with nonlinear identity blocks achieved an inception score of 14.32 and a Fréchet inception distance of 41.86 in the augmenting process. The GAN with identity achieves a smaller value in Fréchet inception distance (FID) and a bigger value in inception score; these results prove the model's efficiency compared with other versions of GAN in the augmentation process. The modified U-Net 3+ architecture achieved a Dice Score of 95.49% and an Accuracy of 95.67%.

Optimization Strategy for Generative Adversarial Networks Design

Generative Adversarial Networks (GANs) are a powerful class of deep learning models that can generate realistic synthetic data. However, designing and optimizing GANs can be a difficult task due to various technical challenges. The article provides a comprehensive analysis of solution methods for GAN performance optimization. The research covers a range of GAN design components, including loss functions, activation functions, batch normalization, weight clipping, gradient penalty, stability problems, performance evaluation, mini-batch discrimination, and other aspects. The article reviews various techniques used to address these challenges and highlights the advancements in the field. The article offers an up-to-date overview of the state-of-the-art methods for structuring, designing, and optimizing GANs, which will be valuable for researchers and practitioners. The implementation of the optimization strategy for the design of standard and deep convolutional GANs (handwritten digits and fingerprints) developed by the authors is discussed in detail, the obtained results confirm the effectiveness of the proposed optimization approach.

Four-channel generative adversarial networks can predict the distribution of reef-associated fish in the South and East China Seas

Reef-associated fish (RAF) are socioeconomically and ecologically important. Herein, we used a four-channel generative adversarial network (GAN) architecture with a retrospective cycle feature to model the distribution of RAF in the East China Sea (ECS) and South China Sea (SCS). Our approach entailed the use of RAF suitability maps and key environmental layers, including sea body temperature, chlorophyll-α concentration, and salinity, as input data. These environmental factors have been demonstrated to significantly influence the distribution of RAF. Our results show that four-channel GANs performed better than one-channel GANs in predicting the distribution of RAF. Furthermore, the proposed four-channel retrospective-cycle GAN (4CRCGAN) model outperformed the commonly used four-channel convolutional GAN (4CCGAN), emphasizing the importance of considering temporal dynamics and feedback loops in predicting the distribution of species. However, it is worth noting that the predictive performance for the distribution of RAF in summer and winter was relatively poor, implying that other environmental and biological factors also exert nonnegligible influences on fish movement and abundance; this underscores the necessity of comprehensively understanding the intricate and dynamic nature of species-environment interactions within marine ecosystems. Our study also revealed that the proposed 4CRCGAN model performed well over multiple steps, which is reflective of its potential to provide valuable insights for decision-makers and stakeholders in the fisheries management sector. Overall, our findings offer critical insights for the development of effective and sustainable management strategies that balance the conservation and sustainable use of these valuable marine resources.

A deep adversarial approach for the generation of synthetic titanium alloy microstructures with limited training data

Microstructures play a central role in determining the mechanical and functional properties of a material. An important aspect in computational materials science is to reliably predict the key microstructural features, and to utilise them in bridging the processing and properties of new materials. The existing microstructure characterization and reconstruction (MCR) techniques have inherent limitations in terms of the lack of design variables and information loss due to various assumptions. In previous studies, Generative Adversarial Network (GAN) models were trained using more than 10,000 image datasets, however without performance analysis using quantitative morphometric and statistical measures. In this perspective, the present work demonstrates the capability of GAN architectures to learn the mapping between random latent vectors and synthetic microstructural images, even in a limited data regime (1225 images). Three different architectures Deep Convolutional GAN (DCGAN) ,Wasserstein GAN- Gradient Penalty (WGAN-GP), StyleGAN2- Adaptive Discriminator Augmentation (ADA) were explored, together with comprehensive statistical and morphological analysis, while training on a publicly accessible Ti-6Al-4V (Ti64) alloy microstructure dataset. The StyleGAN2-ADA outperforms the other two GAN models to generate realistic synthetic microstructures of higher resolution, with good qualitative similarity to original images. The analysis of performance metrics, like Frechet Inception Distance (FID), Kernel Inception Distance (KID), and Inception Score (IS) scores, reveal that the generated image distribution is statistically close to the original distribution of microstructural features. The morphometric parameters, including α/β phase fractions, local α/β boundary thickness, and orientation of lamellar morphology, were also used to compare original and synthetic images quantitatively. More importantly, two-point correlation and t-stochastic neighbour embedding (t-SNE) illustrate the statistical similarity between the original and synthetic microstructures. Taken together, the present work establishes the capability of generative models like GAN in generating representative microstructures of Titanium alloy in a statistically reliable manner. Such an approach, when adopted, will accelerate the field of microstructure fingerprinting.