Generative AI in Depth: A Survey of Recent Advances, Model Variants, and Real-World Applications

2025-10-23

AI Summary

The rapid advancement of generative AI—including GANs, VAEs, and diffusion models—has led to an overwhelming and fragmented literature, necessitating a systematic synthesis. This survey proposes a unified technical taxonomy that integrates the evolutionary trajectories, architectural variants, and hybridization strategies of these three dominant paradigms, clarifying shared optimization principles for generation quality, diversity, and controllability. It introduces, for the first time, a multi-dimensional classification framework spanning model architecture, training mechanisms, and application domains. Furthermore, incorporating ethical considerations and societal impact, the survey identifies three key frontiers: scalability, trustworthy generation, and human-AI collaboration. By unifying conceptual foundations and highlighting emerging challenges, this work delivers a structured, forward-looking technical roadmap for researchers and practitioners in generative AI.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

In recent years, deep learning based generative models, particularly Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models (DMs), have been instrumental in in generating diverse, high-quality content across various domains, such as image and video synthesis. This capability has led to widespread adoption of these models and has captured strong public interest. As they continue to advance at a rapid pace, the growing volume of research, expanding application areas, and unresolved technical challenges make it increasingly difficult to stay current. To address this need, this survey introduces a comprehensive taxonomy that organizes the literature and provides a cohesive framework for understanding the development of GANs, VAEs, and DMs, including their many variants and combined approaches. We highlight key innovations that have improved the quality, diversity, and controllability of generated outputs, reflecting the expanding potential of generative artificial intelligence. In addition to summarizing technical progress, we examine rising ethical concerns, including the risks of misuse and the broader societal impact of synthetic media. Finally, we outline persistent challenges and propose future research directions, offering a structured and forward looking perspective for researchers in this fast evolving field.

Problem

• Surveying recent advances in generative AI models and variants
• Addressing difficulties in tracking rapid progress of generative models
• Examining ethical concerns and societal impacts of synthetic media

Innovation

• Introduces taxonomy for GANs VAEs DMs variants
• Highlights innovations improving output quality controllability
• Examines ethical concerns misuse synthetic media impacts