Improved Mapping Optimization Algorithm based on Generative Adversarial Networks (IMOAG)

<p>High data collection costs and complicated data access regulations increase the demand for synthetic data. Generative Adversarial Networks (GANs) are a novel generative framework with great potential for high quality synthetic data generation. GANs formulate the true distribution of data implicitly, and the success of GANs are often measured based on the similarity of generated data to this true distribution. GANs were originally designed to work with continuous data. However, many important real-world datasets such as medical images involve discontinuous distributions. GAN training for discontinuous distributions is relatively more challenging, as the training procedure often suffers from instability and mode collapse issues. This dissertation focuses on designing novel GAN architectures to generate representative synthetic image data, and proposes new structures to alleviate GANs' mode collapse issue. As part of this thesis, novel applications of image data generation with GANs have been also investigated for important problems arising in the telecommunication industry and medical domain. Specifically, we first explore various GAN structures to generate engineered electromagnetic surfaces. We consider the continuous approximation of the data and explore the capabilities of feed-forward and convolutional GANs for synthetic data generation. Next, we introduce a novel GAN architecture to address the problem of mode collapse in GAN training. The proposed structure incorporates a third network that penalizes the generator for generating low diversity samples. Lastly, we study the challenging problem of object generation in 3D space using GANs, and we propose extensions to existing 3D GAN structures to generate connected 3D volumes. Additionally, we explore a more challenging version of this 3D volume generation problem by generating connected volumes packed with spheres. This research has applications in radiosurgery treatment planning, and the proposed 3D GAN structure can help generate rare, unseen 3D tumor volumes and information on how to treat these tumors. Accordingly, our analysis contributes to overcoming data scarcity issues (e.g., due to privacy considerations) for an important practical problem in the medical domain.</p>

This paper proposes a set of methods that enables low precision posit ™ arithmetic to be successfully used for the training of generative adversarial networks (GANs) with minimal quality loss. We show that ultra low precision posits, as small as 6 bits, can achieve high quality output for the generation phase after training. We also evaluate floating-point (float) formats and compare them to 8-bit posits in the context of GAN training. Our scaling and adaptive calibration techniques are capable of producing superior training quality for 8-bit posits that surpasses 8-bit floats and matches the results of 16-bit floats. Hardware simulation results indicate that our methods have higher energy efficiency compared to both 16- and 8-bit float training systems.

We present a mathematically well-founded approach for the synthetic modeling of turbulent flows using generative adversarial networks (GAN). Based on the analysis of chaotic, deterministic systems in terms of ergodicity, we outline a mathematical proof that GAN can actually learn to sample state snapshots from the invariant measure of the chaotic system. Based on this analysis, we study a hierarchy of chaotic systems starting with the Lorenz attractor and then carry on to the modeling of turbulent flows with GAN. As training data, we use fields of velocity fluctuations obtained from large-eddy simulations (LES). Two architectures are investigated in detail: we use a deep, convolutional GAN (DCGAN) to synthesize the turbulent flow around a cylinder. We furthermore simulate the flow around a low-pressure turbine stator using the pix2pixHD architecture for a conditional DCGAN being conditioned on the position of a rotating wake in front of the stator. The settings of adversarial training and the effects of using specific GAN architectures are explained. We thereby show that GAN are efficient in simulating turbulence in technically challenging flow problems on the basis of a moderate amount of training data. GAN training and inference times significantly fall short when compared with classical numerical methods, in particular, LES, while still providing turbulent flows in high resolution. We furthermore analyze the statistical properties of the synthesized and LES flow fields, which agree excellently. We also show the ability of the conditional GAN to generalize over changes of geometry by generating turbulent flow fields for positions of the wake that are not included in the training data.

Distributed coevolutionary Generative Adversarial Network (GAN) training has empirically shown success in overcoming GAN training pathologies. This is mainly due to diversity maintenance in the populations of generators and discriminators during the training process. The method studied here coevolves sub-populations on each cell of a spatial grid organized into overlapping Moore neighborhoods. We investigate the impact on performance of two algorithm components that influence the diversity during coevolution: the performance-based selection/replacement inside each sub-population and the communication through migration of solutions (networks) among overlapping neighborhoods. In experiments on MNIST dataset, we find that the combination of these two components provides the best generative models. In addition, migrating solutions without applying selection in the sub-populations achieves competitive results, while selection without communication between cells reduces performance.

PSGAN: Revisit the binary discriminator and an alternative for face frontalization

As a new unsupervised learning algorithm framework, generative adversarial networks(GAN) have been favored by more and more researchers, and it has become a research hotspot now. GAN is inspired by the two-person zero-sum game theory in game theory. Its unique adversarial training idea can generate high-quality samples and has more powerful feature learning and feature expression capabilities than traditional machine learning algorithms. At present, GAN has achieved remarkable success in the field of computer vision, especially in the field of sample generation. Every year, a large number of GAN-related research papers are produced, reflecting the fiery degree of research on GAN model. Aiming at the hot model of GAN, first introduce the research status of GAN; then introduce the theory and framework of GAN, which analyzes in detail why the gradient disappears and the mode collapses during the training of GAN; then discussed some typical GAN improvement models, and summarized their theoretical improvements, advantages, limitations, application scenarios and implementation costs; Finally, the application results of GAN in data generation, image super-resolution, and image style conversion are shown, and the current challenges and future research directions of GAN are discussed.

Network architecture search achieves state-of-the-art results in various tasks such as classification and semantic segmentation. Recently, a reinforcement learning-based approach has been proposed for generative adversarial networks (GANs) search. In this work, we propose an alternative strategy for GAN search by using a proxy task instead of common GAN training. Our method is called differentiable efficient generator search, which focuses on efficiently finding the generator in the GAN. Our search algorithm is inspired by the differential architecture search strategy and the global latent optimization procedure. This leads to both an efficient and stable GAN search. After the generator architecture is found, it can be plugged into any existing framework for GAN training. For consistency-term GAN, which we use in this work, the new model outperforms the original inception score results by 0.25 for CIFAR-10.

Training Generative Adversarial Networks (GAN) to generate high-quality images typically requires large datasets. Network pruning during training has recently emerged as a significant advancement for data-efficient GAN. However, simple and straightforward pruning can lead to the risk of losing key information, resulting in suboptimal results due to GAN’s competitive dynamics between generator (G) and discriminator (D). Addressing this, we present RG-GAN, a novel approach that marks the first incorporation of dynamic weight regeneration and pruning in GAN training to improve the quality of the generated samples, even with limited data. Specifically, RG-GAN initiates layer-wise dynamic pruning by removing less important weights to the quality of the generated images. While pruning enhances efficiency, excessive sparsity within layers can pose a risk of model collapse. To mitigate this issue, RG-GAN applies a dynamic regeneration method to reintroduce specific weights when they become important, ensuring a balance between sparsity and image quality. Though effective, the sparse network achieved through this process might eliminate some weights important to the combined G and D performance, a crucial aspect for achieving stable and effective GAN training. RG-GAN addresses this loss of weights by integrating learned sparse network weights back into the dense network at the previous stage during a follow-up regeneration step. Our results consistently demonstrate RG-GAN’s robust performance across a variety of scenarios, including different GAN architectures, datasets, and degrees of data scarcity, reinforcing its value as a generic training methodology. Results also show that data augmentation exhibits improved performance in conjunction with RG-GAN. Furthermore, RG-GAN can achieve fewer parameters without compromising, and even enhancing, the quality of the generated samples. Code can be found at this link: https://github.com/IntellicentAI-Lab/RG-GAN

ABSTRACTData mining and natural language processing (NLP) are fundamental fields that interact in many ways. Text mining shares many topics, such as sentiment analysis and content understanding. Combining these two fields enables more efficient mining of text data and the extraction of valuable information. In particular, the GAN (Generative Adversarial Network) architecture has achieved success in image generation and has started to be used on text data. However, training GANs is fraught with difficulties due to the complexity of text data. Linguistic studies show important differences between languages. Language is characterized by fluidity, ambiguity, and context‐sensitive interpretations, and text‐generating GAN models can struggle to deal with these complexities. The interaction between data quality, language structure, and complex interpretation can lead to inconsistency and ambiguity in the text production of GAN models. These problems are particularly pronounced when complexities such as semantic subtleties, idiomatic expressions, and context‐dependent usages come into play. Text generation is an area of GAN models used in NLP to generate language and enrich text‐based applications. Work in this area can contribute to analyzing, classifying, and processing text data. Many methods and techniques have been proposed to improve the performance of text GANs. However, some problems may be encountered in the optimization of these methods. Therefore, it is essential to use optimized methods. In conclusion, GANs can be an important tool to improve text generation in NLP. Still, they require continuous research and innovation to deal with factors such as language complexity and data quality.

In response to the problems of traditional genetic algorithms in evaluating English compositions, the stability of automatic grading of English compositions has been further enhanced. This article evaluates the teaching effectiveness of automatic grading of English compositions using an optimization fusion algorithm combined with genetic optimization algorithm and CNN model. By analyzing genetic content and optimization algorithms, a corresponding fusion optimization model was obtained, and the automatic evaluation of English compositions was analyzed and predicted through experimental verification. The results indicate that the curves corresponding to different parameters exhibit typical segmentation features through the variation curves of individual numbers under different scale factors. And through quantitative description and analysis of the curve, it can be seen that the change in proportion factor has an absolute advantage in the impact of genetic algorithm on the number of children. As the number of samples increases, the performance of genetic optimization algorithms under the f function shows an upward trend. Research has shown that the writing content index has the greatest impact on English writing, while the corresponding grammar errors have the smallest impact on English writing. Finally, the accuracy of the optimized model was verified by comparing the model curve with experimental data. This study provides theoretical support for the use of genetic optimization algorithms and CNN models in English, and provides ideas for the use of optimization algorithms in other fields.

Generative Adversarial Networks (GANs) are highly effective for generating realistic images, yet their training can be unstable due to challenges such as mode collapse and oscillatory convergence. In this paper, we propose a novel hybrid optimization method that integrates Genetic Algorithms (GAs) to improve the training process of Deep Convolutional GANs (DCGANs). Specifically, GAs are used to evolve the discriminator’s weights, complementing the gradient-based learning typically employed in GANs. The proposed GAGAN model is trained on the CelebA dataset, using 2000 images, to generate 128 × 128 images, with the generator learning to produce realistic faces from random latent vectors. The discriminator, which classifies images as real or fake, is optimized not only through standard backpropagation, but also through a GA framework that evolves its weights via crossover, mutation, and selection processes. This hybrid method aims to enhance convergence stability and boost image quality by balancing local search from gradient-based methods with the global search capabilities of GAs. Experiments show that the proposed approach reduces generator loss and improves image fidelity, demonstrating that evolutionary algorithms can effectively complement deep learning techniques. This work opens new avenues for optimizing GAN training and enhancing performance in generative models.

Generative Adversarial Networks (GANs) have proven to be a preferred method of synthesizing fake images of ob-jects, such as faces, animals, and automobiles. It is not surprising these models can also generate ISO-compliant, yet synthetic iris images, which can be used to augment training data for iris matchers and liveness detectors. In this work, we trained one of the most recent GAN mod-els (StyleGAN3 [15]) to generate fake iris images with two primary goals: (i) to understand the GAN's ability to produce "never-before-seen" irises, and (ii) to investigate the phenomenon of identity leakage as a function of the GAN's training time. Previous work has shown that personal biometric data can inadvertently flow from training data into synthetic samples, raising a privacy concern for subjects who accidentally appear in the training dataset. This paper presents analysis for three different iris matchers at varying points in the GAN training process to diagnose where and when authentic training samples are in jeopardy of leaking through the generative process. Our results show that while most synthetic samples do not show signs of identity leak-age, a handful of generated samples match authentic (training) samples nearly perfectly, with consensus across all matchers. In order to prioritize privacy, security, and trust in the machine learning model development process, the re-search community must strike a delicate balance between the benefits of using synthetic data and the corresponding threats against privacy from potential identity leakage.

Generative Adversarial Networks (GANs) were first used to generate images that were similar to images in the data the model was trained on. The GANs training is based on a zero-sum game where the constituent models are adversaries. The mathematical interpretation of GAN training is the mapping of an unknown distribution to the dataset distribution. Future works in the field led to the generation of music, texts, and types of data and GANs still are being explored in scientific, entertainment, fashion, advertising, videogames, and other miscellaneous applications. This review focuses on the versatility of GANs. First, the GAN model is explored with its mathematical intuition. Then come the popular variants of GANs and their applications. Finally, the most recent applications of GANs in different fields are discussed, and the review ends with a discussion of future possible applications of GANs.

Hyperspectral imagery (HSI) cubes are high-dimensional datasets that lend themselves well to deep learning approaches for classification. Deep learning approaches, specifically generative adversarial networks (GANs), have been shown to be very effective in classification and generation of accurate synthetic data in computer vision problems. This work proposes an extension of an existing GAN training scheme, called extended semi-supervised learning (ESSL), metrics for evaluating GAN training performance, and demonstrates the effectiveness of the proposed training scheme to improve classification of HSI. Using ESSL with GAN, we have been able to achieve approximately 0.8% increase in classification accuracy over convolutional neural networks as well as generate extremely accurate synthetic imagery.

Generative Adversarial Network (GANs) is widely implemented not just for image generation but also text, audio, video processing, and more. To add on, methods of image restoration and enhancement such as semantic inpainting, outpainting, filtering, and denoising are achievable by GANs. This paper reviews the theoretical basis of GANs in image generation, also known as image synthesis, and possibilities of successful restoration of historical illustrations such as buildings, landmarks, and cultural heritage that have been lost or demolished. By training GANs on a relevant dataset of images, it can learn characteristics and features of the dataset to generate desired results based on raw image as input. Theoretical knowledge about GANs and relevancy of study is elaborated. For methodology, random sampling and stratified sampling are applied for data collection with justifications together with limitations. The proposed system, HistoGAN, restores damaged or incomplete images through image synthesis after being trained with large datasets. Performance of implementation is analyzed and evaluated to identify space of improvement to uplift performance. HistoGAN has the potential to be applicable in other fields such as historical research, architecture discovery, and education.