Jul 2026· International Journal For Multidisciplinary Research· Vol 8· 0 citations· 9 references
TL;DR
A critical and comparative review of ten research papers, each offering a unique solution to the core issues of GAN training instability, indicate that no single method dominates across all scenarios, but hybrid combinations lead to the best generalizability and stability under constraints like limited data, non IID settings, or real-time computation.
Abstract
Generative Adversarial Networks (GANs) have become the backbone of data synthesis across multiple domains like healthcare, audio processing, real-time systems, and federated learning. Despite their revolutionary potential, GANs suffer from major challenges in stability, scalability, and data dependence. This seminar presents a critical and comparative review of ten research papers, each offering a unique solution to the core issues of GAN training instability. The reviewed methods encompass domain adaptation, feature distillation, theoretical regularization, adaptive augmentation, and hierarchical federated architectures. This report provides an in-depth literature analysis, followed by a cross-domain metric-wise performance comparison. The results indicate that no single method dominates across all scenarios, but hybrid combinations lead to the best generalizability and stability under constraints like limited data, non IID settings, or real-time computation. Findings are contextualized in terms of practical impact, with implications for the future design of robust, scalable GAN architectures.
Various strategies and improvements to enhance GANs stability and performance are examined, including hybrid architectures that integrate GANs with other deep learning models and practical utility in domain‐specific expert systems.
An in-depth and up- to-date overview of the GANs environment, principally highlighting the progress made over 2020 and beyond and proposing the idea of hybrid generative systems in the future while emphasizing the oppositional approach's extraordinary and enduring features.
Zahraa Salah Dhaif, H. J. Serteep· International Journal of Adv...· 0 citations
Data-driven deep learning models have revolutionized the ability to understand and model intrinsic data patterns. However, in real-world applications, their reliability is often compromised by inherent randomness, uncertainty, and potential adversarial threats, particularly those originating from data. As a critical component of data engineering, data perturbation has emerged as an effective approach for evaluating and enhancing the robustness of deep learning models, encompassing both inadvertent and deliberate modifications to data. This survey offers a comprehensive review of data perturbations, with a particular focus on the extensively studied image perturbations on classification throughout the development and deployment of deep models. It presents a detailed summary and taxonomy of existing perturbations, their generation methods, interrelationships, implications for model robustness, and explores underlying mechanisms. By systematically analyzing recent advances and best practices across both digital and physical domains, our goal is to provide an in-depth understanding of how data engineering, which is particularly through data perturbation, can be leveraged to develop models that are both accurate and resilient. Additionally, we discuss current limitations in current research and suggest promising directions for future study.
Pengfei Zhang, Guangdong Bai, Xinshun Xu et al.· IEEE Transactions on Knowled...· 0 citations
The review shows that diffusion and autoregressive foundation models increasingly dominate high-fidelity image, language, and multimodal generation, while GANs, VAEs, and flow-based models remain important in data-limited, structured, scientific, and privacy-aware settings.
A. Javadpour, F. Ja’fari, T. Taleb et al.· IEEE Access· 0 citations
Overall, this dissertation provides a unified investigation into data imbalance, data quality, and data scarcity-three core bottlenecks of modern deep learning-and proposes principled solutions that improve robustness, interpretability, and efficiency across both CV and NLP domains.
Extensive experiments on multiple bearing fault datasets demonstrate that CSR-DGAN outperforms existing generative augmentation methods in terms of distribution similarity, cross-domain consistency, and downstream diagnostic performance, highlighting the effectiveness of the proposed problem-driven dual-domain generative framework for robust fault diagnosis under imbalanced conditions.
Lifang Chen, Zihan Ren, Lingjing Kong et al.· International Journal of Dat...· 0 citations
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