Generative Adversarial Networks Research Articles

Data augmentation and simultaneous prediction of optical properties of a porous core single mode fiber: CTGAN in the domain of optics

Abstract Photonic crystal fiber (PCF) architectures have garnered significant interest due to their versatile applications across various fields. However, the complexity of PCF designs and the computational demands of full vectorial finite element method (FV-FEM) simulations pose challenges in fully realizing their potential. While prior research has explored artificial neural networks (ANNs) to accelerate simulation predictions, proposed approaches were often limited by sample size and generalizability. In this manuscript we introduce conditional generative adversarial networks (CTGAN) to augment real datasets, facilitating more effective ANN training. We evaluate CTGAN’s performance by comparing the quality of augmented data with real data and assess the predictive accuracy of ANNs trained on both augmented and real datasets. Our results demonstrate enhanced predictive accuracy, with higher R 2 values for optical property predictions from structural parameters using the augmented dataset-trained ANN. Furthermore, the mean squared error (MSE) during ANN training decreased significantly (from 0.0051 to 0.0011), requiring fewer convergence epochs of only 88 compared to 114 for the real dataset. The proposed approach enables faster optical property predictions while reducing the required dataset generation simulations by up to 27.9 %.

C 3 -GAN+: Complex-Condition-Controlled Generative Adversarial Networks with Enhanced Embedding

Given historical traffic distributions and associated urban conditions observed in a city, the conditional urban traffic estimation problem aims at estimating realistic future projections of the traffic under a set of new urban conditions, e.g., new bus routes, rainfall intensity, and travel demands. The problem is important in reducing traffic congestion, improving public transportation efficiency, and facilitating urban planning. However, solving this problem is challenging due to the strong spatial dependencies of traffic patterns and the complex relations between the traffic and urban conditions. Recently, we proposed a Complex-Condition-Controlled Generative Adversarial Network ( \(\boldsymbol{C^{3}}\) -GAN) , which tackles both of the challenges and solves the urban traffic estimation problem under various complex conditions by adding a fixed embedding network and an inference network on top of the standard conditional GAN model. The randomly chosen embedding network transforms the complex conditions to latent vectors, and the inference network enhances the connections between the embedded vectors and the traffic data. However, a randomly chosen embedding network cannot always successfully extract features of complex urban conditions, which indicates \(C^{3}\) -GAN is unable to uniquely map different urban conditions to proper latent distributions. Thus, \(C^{3}\) -GAN would fail in certain traffic estimation tasks. Besides, \(C^{3}\) -GAN is hard to train due to vanishing gradients and mode collapse problems. To address these issues, in this article, we extend our prior work by introducing a new deep generative model, namely, \(C^{3}\) -GAN \(+\) , which significantly improves the estimation performance and model stability. \(C^{3}\) -GAN \(+\) has new objective, architecture, and training algorithm. The new objective applies Wasserstein loss to the conditional generation case to encourage stable training. Shared convolutional layers between the discriminator and the inference network help to capture spatial dependencies of traffic more efficiently, part of the shared convolutional layers are used to update the embedding network periodically aiming to encourage good representation and avoid model divergence. Extensive experiments on real-world datasets demonstrate that our \(C^{3}\) -GAN \(+\) produces high-quality traffic estimations and outperforms state-of-the-art baseline methods.

Generative Artificial Intelligence and the Evolving Challenge of Deepfake Detection: A Systematic Analysis

Deepfake technology, which employs advanced generative artificial intelligence to create hyper-realistic synthetic media, poses significant challenges across various sectors, including security, entertainment, and education. This literature review explores the evolution of deepfake generation methods, ranging from traditional techniques to state-of-the-art models such as generative adversarial networks and diffusion models. We navigate through the effectiveness and limitations of various detection approaches, including machine learning, forensic analysis, and hybrid techniques, while highlighting the critical importance of interpretability and real-time performance in detection systems. Furthermore, we discuss the ethical implications and regulatory considerations surrounding deepfake technology, emphasizing the need for comprehensive frameworks to mitigate risks associated with misinformation and manipulation. Through a systematic review of the existing literature, our aim is to identify research gaps and future directions for the development of robust, adaptable detection systems that can keep pace with rapid advancements in deepfake generation.

Applications of Machine Learning-Driven Molecular Models for Advancing Ophthalmic Precision Medicine

Ophthalmic diseases such as glaucoma, age-related macular degeneration (ARMD), and optic neuritis involve complex molecular and cellular disruptions that challenge current diagnostic and therapeutic approaches. Advanced artificial intelligence (AI) and machine learning (ML) models offer a novel lens to analyze these diseases by integrating diverse datasets, identifying patterns, and enabling precision medicine strategies. Over the past decade, applications of AI in ophthalmology have expanded from imaging-based diagnostics to molecular-level modeling, bridging critical gaps in understanding disease mechanisms. This paper systematically reviews the application of AI-driven methods, including reinforcement learning (RL), graph neural networks (GNNs), Bayesian inference, and generative adversarial networks (GANs), in the context of these ophthalmic conditions. RL models simulate transcription factor dynamics in hypoxic or inflammatory environments, offering insights into disrupted molecular pathways. GNNs map intricate molecular networks within affected tissues, identifying key inflammatory or degenerative drivers. Bayesian inference provides probabilistic models for predicting disease progression and response to therapies, while GANs generate synthetic datasets to explore therapeutic interventions. By contextualizing these AI tools within the broader framework of ophthalmic disease management, this review highlights their potential to transform diagnostic precision and therapeutic outcomes. Ultimately, this work underscores the need for continued interdisciplinary collaboration to harness AI’s potential in advancing the field of ophthalmology and improving patient care.

A method for reconstruction of structural health monitoring data using WGANGP with U-net generator

Data loss issue often occurs in structural health monitoring (SHM), which can undermine the reliability of structural condition diagnosis and prognosis. Neural networks are commonly employed for reconstruction of missing SHM data by modeling the correlation between intact and missing signals. The existing neural network methods use deeper architectures to capture complex data correlations. As network depth increases, the ability to preserve both low- and high-level features diminishes, and network training becomes challenging, which reduces data reconstruction accuracy. This study proposes a data reconstruction method that utilizes a Wasserstein generative adversarial network containing a gradient penalty term with a U-net generator. Multiple improvements are made to the generative adversarial network to enhance the reconstruction performance. First, the U-net is used as a generator, and the signal features at low and high levels are preserved based on the skip-connection technique. The U-net is pre-set with multiple layers to enhance the reconstruction accuracy. Second, a mean square error (MSE) term is added to the loss function. The MSE coefficient is proposed to balance adversarial training and reconstruction feature learning. Third, the Wasserstein distance is introduced to replace the cross-entropy loss function of traditional generative adversarial networks, avoiding gradient vanishing and exploding. The reconstruction performance of the proposed method is evaluated on a computational bridge model and Canton Tower, and the influence of reconstruction parameters on the accuracy of reconstruction results is discussed in detail. The reconstructed data by the proposed method closely matches the original data in both the time domain and frequency domain. The time–frequency characteristics of the acceleration data can be accurately reconstructed, demonstrating the effectiveness of the reconstructed signals in data analysis. By comparing with typical neural network methods, it is found that the proposed method has higher reconstruction accuracy.

Transformer-based image and video inpainting: current challenges and future directions

Image inpainting is currently a hot topic within the field of computer vision. It offers a viable solution for various applications, including photographic restoration, video editing, and medical imaging. Deep learning advancements, notably convolutional neural networks (CNNs) and generative adversarial networks (GANs), have significantly enhanced the inpainting task with an improved capability to fill missing or damaged regions in an image or a video through the incorporation of contextually appropriate details. These advancements have improved other aspects, including efficiency, information preservation, and achieving both realistic textures and structures. Recently, Vision Transformers (ViTs) have been exploited and offer some improvements to image or video inpainting. The advent of transformer-based architectures, which were initially designed for natural language processing, has also been integrated into computer vision tasks. These methods utilize self-attention mechanisms that excel in capturing long-range dependencies within data; therefore, they are particularly effective for tasks requiring a comprehensive understanding of the global context of an image or video. In this paper, we provide a comprehensive review of the current image/video inpainting approaches, with a specific focus on Vision Transformer (ViT) techniques, with the goal to highlight the significant improvements and provide a guideline for new researchers in the field of image/video inpainting using vision transformers. We categorized the transformer-based techniques by their architectural configurations, types of damage, and performance metrics. Furthermore, we present an organized synthesis of the current challenges, and suggest directions for future research in the field of image or video inpainting.

A Data-Driven Approach for Automatic Aircraft Engine Borescope Inspection Defect Detection Using Computer Vision and Deep Learning

Regular aircraft engine inspections play a crucial role in aviation safety. However, traditional inspections are often performed manually, relying heavily on the judgment and experience of operators. This paper presents a data-driven deep learning framework capable of automatically detecting defects on reactor blades. Specifically, this study develops Deep Neural Network models to detect defects in borescope images using various datasets, based on Computer Vision and YOLOv8n object detection techniques. Firstly, reactor blade images are collected from public resources and then annotated and preprocessed into different groups based on Computer Vision techniques. In addition, synthetic images are generated using Deep Convolutional Generative Adversarial Networks and a manual data augmentation approach by randomly pasting defects onto reactor blade images. YOLOv8n-based deep learning models are subsequently fine-tuned and trained on these dataset groups. The results indicate that the model trained on wide-shot blade images performs better overall at detecting defects on blades compared to the model trained on zoomed-in images. The comparison of multiple models’ results reveals inherent uncertainties in model performance that while some models trained on data enhanced by Computer Vision techniques may appear more reliable in some types of defect detection, the relationship between these techniques and subsequent results cannot be generalized. The impact of epochs and optimizers on the model’s performance indicates that incorporating rotated images and selecting an appropriate optimizer are key factors for effective model training. Furthermore, models trained solely on artificially generated images from collages perform poorly at detecting defects in real images. A potential solution is to train the model on both synthetic and real images. Future work will focus on improving the framework’s performance and conducting a more comprehensive uncertainty analysis by utilizing larger and more diverse datasets, supported by enhanced computational power.

Anomaly Detection in Network Access-Using LSTM and Encoder-Enhanced Generative Adversarial Networks

Anomaly Detection in Network Access-Using LSTM and Encoder-Enhanced Generative Adversarial Networks

Unseen Rail Damages Diagnosis Framework Using Mechanism Embedded Generative Network

Abstract Rail damages can pose tremendous hazards for high-speed trains, making damage diagnosis critical in the field of engineering. Currently, deep learning enables an end-to-end approach for rail damage diagnosis. However, the training and test data in real applications are often out of distribution, or even the test data represent fault categories that are previously unseen. To address this situation, an unseen damages diagnosis framework (UDDF) that effectively embeds the mechanism damage features from the simulation signals of all possible damage categories has been proposed. In particular, the mechanism- embedded generative adversarial networks in the UDDF utilize a hierarchical embedding technique to ensure the stability of the mechanism embedding process. In addition, a k-means clustering discriminator uses an unsupervised method to guarantee the minimum intra-category sample spacing of the generated unseen categories. After the generation of all types of damage categories, the generated and existing original data are included as a new dataset for the training of a diagnostic model. The trained diagnostic model can perform classification tasks without acquiring all the types of damage signals in real situations. Finally, the effectiveness of our proposed diagnostic framework is validated through comparative and ablation studies on a dataset that contains finite element simulation and experimental data of ultrasonic guided waves signals with damages at different locations and depths of rails.

Patient- and fraction-specific magnetic resonance volume reconstruction from orthogonal images with generative adversarial networks.

Although deep learning (DL) methods for reconstructing 3D magnetic resonance (MR) volumes from 2D MR images yield promising results, they require large amounts of training data to perform effectively. To overcome this challenge, fine-tuning-a transfer learning technique particularly effective for small datasets-presents a robust solution for developing personalized DL models. A 2D to 3D conditional generative adversarial network (GAN) model with a patient- and fraction-specific fine-tuning workflow was developed to reconstruct synthetic 3D MR volumes using orthogonal 2D MR images for online dose adaptation. A total of 2473 3D MR volumes were collected from 43 patients. The training and test datasets were separated into 34 and 9 patients, respectively. All patients underwent MR-guided adaptive radiotherapy using the same imaging protocol. The population data contained 2047 3D MR volumes from the training dataset. Population data were used to train the population-based GAN model. For each fraction of the remaining patients, the population model was fine-tuned with the 3D MR volumes acquired before beam irradiation of the fraction, named the fine-tuned model. The performance of the fine-tuned model was tested using the 3D MR volume acquired immediately after the beam delivery of the fraction. The model's input was a pair of axial and sagittal MR images at the isocenter level, and the output was a 3D MR volume. Model performance was evaluated using the structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and mean absolute error (MAE). Moreover, the prostate, bladder, and rectum in the predicted MR images were manually segmented. To assess geometric accuracy, the 2D Dice Similarity Coefficient (DSC) and 2D Hausdorff Distance (HD) were calculated. A total of 84 3D MR volumes were included in the performance testing. The mean±standard deviation (SD) of SSIM, PSNR, RMSE, and MAE were 0.64±0.10, 93.9±1.5dB, 0.050±0.009, and 0.036±0.007 for the population model and 0.72±0.09, 96.2±1.8dB, 0.041±0.007, and 0.028±0.006 for the fine-tuned model, respectively. The image quality of the fine-tuned model was significantly better than that of the population model (p<0.05). The mean±SD of DSC and HD of the population model were 0.79±0.08 and 1.70±2.35mm for prostate, 0.81±0.10 and 2.75±1.53mm for bladder, and 0.72±0.08 and 1.93±0.59mm for rectum. Contrarily, the mean±SD of DSC and HD of the fine-tuned model were 0.83±0.06 and 1.29±0.77mm for prostate, 0.85±0.07 and 2.16±1.09mm for bladder, and 0.77±0.08 and 1.57±0.52mm for rectum. The geometric accuracy of the fine-tuned model was significantly improved than that of the population model (p<0.05). By employing a patient- and fraction-specific fine-tuning approach, the GAN model demonstrated promising accuracy despite limited data availability.

Multimedia interactive creative dance choreography integrating intelligent chaotic art algorithms

Creative dance choreography involves the exploration of movement as a form of artistic expression, often characterized by innovative and spontaneous elements. Interactive multimedia enhances the art form by integrating technology, enabling real-time audience engagement and dynamic visual effects. Traditional choreography often adheres to fixed patterns, which can limit improvisation and reduce audience participation. The objective of the study is to integrate the chaotic art algorithm within multimedia interactive dance choreography, enhancing both the creative process and audience engagement while producing novel movement patterns. Data were collected through motion capture technology and video recordings. The preprocessing phase included noise reduction and normalization of the movement data. Feature extraction using principal component analysis (PCA) techniques analyzed the captured data, identifying key attributes such as speed, trajectory, and synchronization. The study proposed a simulation-based intelligent chaotic optimized generative adversarial network (ICO-GAN) that enhances dance choreography by generating diverse, unpredictable movement patterns, optimizing performance through chaotic art algorithms, and fostering innovative, engaging interactions between dancers and multimedia elements. The result shows that the proposed ICO-GAN method enriched the choreography by generating diverse movement patterns that were seamlessly incorporated into performances based on precision (94%), recall (92%), accuracy (97%), and F1-score (93%). This innovative approach opens new avenues for exploration in dance and technology, offering expanded possibilities for artistic expression and interaction.

Computer-aided cholelithiasis diagnosis using explainable convolutional neural network.

Accurate and precise identification of cholelithiasis is essential for saving the lives of millions of people worldwide. Although several computer-aided cholelithiasis diagnosis approaches have been introduced in the literature, their use is limited because Convolutional Neural Network (CNN) models are black box in nature. Therefore, a novel approach for cholelithiasis classification using custom CNN with post-hoc model explanation is proposed. This paper presents multiple contributions. First, a custom CNN architecture is proposed to classify and predict cholelithiasis from ultrasound image. Second, a modified deep convolutional generative adversarial network is proposed to produce synthetic ultrasound images for better model generalization. Third, a hybrid visual explanation method is proposed by combining gradient-weighted class activation with local interpretable model agnostic explanation to generate a visual explanation using a heatmap. Fourth, an exhaustive performance analysis of the proposed approach on ultrasound images collected from three different Indian hospitals is presented to showcase its efficacy for computer-aided cholelithiasis diagnosis. Fifth, a team of radiologists evaluates and validates the prediction and respective visual explanations made using the proposed approach. The results reveal that the proposed cholelithiasis classification approach beats the performance of state-of-the-art pre-trained CNN and Vision Transformer models. The heatmap generated through the proposed hybrid explanation method offers detailed visual explanations to enhance transparency and trustworthiness in the medical domain.

Utilizing generative AI for crack detection in the marble industry

Abstract Recently, deep learning algorithms have achieved great accuracies in multiple computer vision tasks by using considerable amounts of annotated data. In the case of marble surface crack detection, the formation of such a dataset is challenging. Currently, there are limited marble crack-related datasets, obstructing the development of robust inspection models for automated industrial quality control. To resolve this problem, we propose a generative Artificial Intelligence (AI) approach, reporting the first synthetic marble crack texture image dataset using four generative adversarial networks (GANs). Each of the final four sub-datasets includes images of different cracks on marble surfaces. The quality of the synthetic datasets is comparatively evaluated; quantitative and qualitative experimental results are provided, highlighting the most efficient generative method. Moreover, to verify the effectiveness and significance of this work, two deep learning segmentation models are used to assess the quality of the synthetic marble crack image datasets. Results indicate that different training strategies using synthetic data could boost the performance of segmentation by up to 6.55% compared to conventional data augmentation techniques. Thus, the first step towards deep learning-based automated marble crack segmentation is delivered, aiming for the simultaneous robotic resin application for healing cracks on-site, in marble-processing industrial settings.

Data Augmentation Techniques Using Generative Adversarial Networks: Employing GANs to Create Synthetic Data for Enhancing Machine Learning Model Training

The capability of GANs to increase data size has been a revelation to people in the augmentation field. It has saved machine learning algorithms with scarcity, imbalance, or poor data variation. Unlike the previous approaches based on transformation techniques, GANs can learn complex patterns and generate realistic synthetic data, which have virtually never been possible for fields and applications. In this paper, 12 new GAN-augmentation strategies are proposed and implemented. These are image-to-image translation, text-to-image synthesis, style transfer, and domain adaptation. These techniques prove that using GANs can enhance the quality of the data and models, balance the data, and maintain the confidentiality of the data while applying any of these techniques. The use of GANs in generating new data to enrich data sets has proved promising, especially in specific fields like health, where medical images complement diagnostic models while respecting patients' rights to privacy. It has also been used in image recognition when it creates diverse outputs to solve problems such as class imbalance, besides solving regular problems faced in natural language processing, including text-to-image synthesis. Compared to previous approaches, GANs provide better and more complex solutions and fill the data with newcomers instead of transforming the given ones. This capability leads to the creation of much more diverse datasets that can be closer to the real environment. Nevertheless, there are some key issues of concern, such as computational cost, mode collapse, and, most notably, social impact issues, where GANs may be misused using deepfake technologies. The proposed techniques show great potential and are based on a new approach to using data augmentation to solve modern tasks. However, the lack of detailed experiments and decreased accuracy compared to machine learning algorithms emphasize the need for future research to confirm the efficiency of the proposed approach in various applications. Overcoming these limitations via solid architectures, appropriate measures of performance, and policies is crucial. While GANs are still a relatively young type of AI technology, their enhancement and scaling can create significant opportunities for using these models in combination with other complex models, such as diffusion and reinforcement learning.

A Review of Deep Learning Applications in Intrusion Detection Systems: Overcoming Challenges in Spatiotemporal Feature Extraction and Data Imbalance

In the rapid development of the Internet of Things (IoT) and large-scale distributed networks, Intrusion Detection Systems (IDS) face significant challenges in handling complex spatiotemporal features and addressing data imbalance issues. This article systematically reviews recent advancements in applying deep learning techniques in IDS, focusing on the core challenges of spatiotemporal feature extraction and data imbalance. First, this article analyzes the spatiotemporal dependencies of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in network traffic feature extraction and examines the main methods these models use to solve this problem. Next, the impact of data imbalance on IDS performance is explored, and the effectiveness of various data augmentation and handling techniques, including Generative Adversarial Networks (GANs) and resampling methods, in improving the detection of minority class attacks is assessed. Finally, the paper highlights the current research gaps and proposes future research directions to optimize deep learning models further to enhance the detection capabilities and robustness of IDS in complex network environments. This review provides researchers with a comprehensive perspective, helping them identify the challenges in the current field and laying a foundation for future research efforts.

Data-driven signal-to-noise enhancement in scattering near-field infrared microscopy.

This study introduces a machine-learning approach to enhance signal-to-noise ratios in scattering-type scanning near-field optical microscopy (s-SNOM). While s-SNOM offers a high spatial resolution, its effectiveness is often hindered by low signal levels, particularly in weakly absorbing samples. To address these challenges, we utilize a data-driven "patch-based" machine learning reconstruction method, incorporating modern generative adversarial neural networks (CycleGANs) for denoising s-SNOM images. This method allows for flexible reconstruction of images of arbitrary sizes, a critical capability given the variable nature of scanned sample areas in point-scanning probe-based microscopies. The CycleGAN model is trained on unpaired sets of images captured at both rapid and extended acquisition times, thereby modeling instrument noise while preserving essential topographical and molecular information. The results show significant improvements in image quality, as indicated by higher structural similarity index and peak signal-to-noise ratio values, comparable to those obtained from images captured with four times the integration time. This method not only enhances image quality but also has the potential to reduce the overall data acquisition time, making high-resolution s-SNOM imaging more feasible for a wide range of biological and materials science applications.

Machine-learning-driven semiauto multiscenario horizon interpretation with uncertainty quantification

Horizon interpretation is one of the most labor-intensive and interpreter-dependent processes in the reservoir characterization workflow. This issue becomes critical, particularly when the quality of a seismic reflector is poor. The uncertainty caused by inaccurate seismic horizon interpretations often misleads the volumetric analyses of subsurface reservoirs. To resolve these issues, we develop a four-step machine-learning-based semiautomated horizon interpretation workflow. In the first step, we generate multiple virtual stratigraphic labels. We then generate multiple synthetic data points to mimic the features of field data by applying a cycle-consistent generative adversarial network. In the third step, we train a U-Net augmented with feature pyramid attention and a convolutional block attention module on the synthetic data set. Finally, the probabilistic distribution results are used to obtain multiple horizon selection scenarios, yielding various possible stratigraphic results. As our method yields a probability distribution rather than a single value for a given field data and seed point, we can obtain multiple possible horizon scenarios rather than a single horizon. Through uncertainty quantification, we can select an appropriate horizon among multiple scenarios. We apply our method to the Smeaheia field data set, which is characterized by numerous and large faults, and compare the results with reference interpretation to confirm its accuracy. The field data example indicates that our method can be applied to complex geologic structures.

Pano-GAN: A Deep Generative Model for Panoramic Dental Radiographs

This paper presents the development of a generative adversarial network (GAN) for the generation of synthetic dental panoramic radiographs. While this is an exploratory study, the ultimate aim is to address the scarcity of data in dental research and education. A deep convolutional GAN (DCGAN) with the Wasserstein loss and a gradient penalty (WGAN-GP) was trained on a dataset of 2322 radiographs of varying quality. The focus of this study was on the dentoalveolar part of the radiographs; other structures were cropped out. Significant data cleaning and preprocessing were conducted to standardize the input formats while maintaining anatomical variability. Four candidate models were identified by varying the critic iterations, number of features and the use of denoising prior to training. To assess the quality of the generated images, a clinical expert evaluated a set of generated synthetic radiographs using a ranking system based on visibility and realism, with scores ranging from 1 (very poor) to 5 (excellent). It was found that most generated radiographs showed moderate depictions of dentoalveolar anatomical structures, although they were considerably impaired by artifacts. The mean evaluation scores showed a trade-off between the model trained on non-denoised data, which showed the highest subjective quality for finer structures, such as the mandibular canal and trabecular bone, and one of the models trained on denoised data, which offered better overall image quality, especially in terms of clarity and sharpness and overall realism. These outcomes serve as a foundation for further research into GAN architectures for dental imaging applications.

A Novel Face Frontalization Method by Seamlessly Integrating Landmark Detection and Decision Forest into Generative Adversarial Network (GAN)

In real-world scenarios, posture variation and low-quality image resolution are two well-known factors that compromise the accuracy and reliability of face recognition system. These challenges can be overcome using various methods, including Generative Adversarial Networks (GANs). Despite this, concerns over the accuracy and reliability of GAN methods are increasing as the facial recognition market expands rapidly. The existing framework such as Two-Pathway GAN (TP-GAN) method has demonstrated that it is superior to numerous GAN methods that provide better face-texture details due to its unique deep neural network structure that allows it to perceive local details and global structure in a supervised manner. TP-GAN overcomes some of the obstacle associated with face frontalization tasks through the use of landmark detection and synthesis functions, but it remains challenging to achieve the desired performance across a wide range of datasets. To address the inherent limitations of TP-GAN, we propose a novel face frontalization method (NFF) combining landmark detection, decision forests, and data augmentation. NFF provides 2D landmark detection to integrate global structure with local details of the generator model so that more accurate facial feature representations and robust feature extractions can be achieved. NFF enhances the stability of the discriminator model over time by integrating decision forest capabilities into the TP-GAN discriminator core architecture that allows us to perform a wide range of facial pose tasks. Moreover, NFF uses data augmentation techniques to maximize training data by generating completely new synthetic data from existing data. Our evaluations are based on the Multi-PIE, FEI, and CAS-PEAL datasets. NFF results indicate that TP-GAN performance can be significantly enhanced by resolving the challenges described above, leading to high quality visualizations and rank-1 face identification.

Optimizing Bandwidth Allocation in Converged Networks Using Hybrid Deep Learning Model

The demand for high-speed network services and the increasing development of network traffic have led to the popularity of converged networks, which mix various services over a single infrastructure. However, because of the variety of application requirements and resource constraints, ensuring quality of service (QoS) in these networks is difficult. Conventional methods for allocating bandwidth are frequently static, reactive, and inefficient, which results in less-than-ideal network performance. We provide a unique deep learning method to optimize bandwidth allocation in convergent networks in order to overcome this. We create and use three deep learning models: Deep Q-Networks (DQN), Generative Adversarial Networks (GAN), and a special LSTM-based DQN model. We assess each model's performance using an extensive dataset. Our results show that the Novel DQN model performs better than the other models in terms of minimum packet loss, increased accuracy, decreased latency, throughput maximization, spectral efficiency optimization, bit error rate reduction, fairness assurance, and effective channel resource use. Better service quality is the outcome of these upgrades, which also significantly increase upload and download speeds. Our empirical studies demonstrate our methodology's usefulness in real-world scenarios and open the door to intelligent network management solutions that facilitate better QoS, efficient bandwidth allocation, and improved user experiences in converged networks.