Introduction to Diffusion Models, Autoencoders and Transformers: Review of Current Advancements

Generative Artificial Intelligence (GAI) has emerged as a transformative technology, enabling machines to create new content such as text, images, audio, and video that mimics human-like creativity. This paper provides a comprehensive review of the most influential generative AI models, including Generative Adversarial Networks (GANs), Transformers, Autoencoders, Diffusion Models, and Variational Autoencoders (VAEs). We explore their theoretical foundations, practical implementations, and applications across various domains such as healthcare, entertainment, education, and business. GANs, introduced in 2014, have revolutionized image generation and synthetic data creation through adversarial training, while Transformers, particularly models like GPT-3 and GPT-4, have redefined natural language processing (NLP) with their self-attention mechanisms. Diffusion models, which generate data by reversing a noise-adding process, have gained prominence for their ability to produce high-quality outputs with stable training. Autoencoders and VAEs, on the other hand, are widely used for dimensionality reduction, feature extraction, and probabilistic data generation. Furthermore, we discuss the role of synthetic data generation in overcoming data scarcity and privacy issues, highlighting techniques such as GANs, VAEs, and diffusion models. The paper concludes with a forward-looking perspective on the future of generative AI, emphasizing the importance of efficient sampling methods, theoretical advancements, and multimodal applications to unlock the full potential of these technologies. This review serves as a valuable resource for researchers and practitioners, offering insights into the current state of generative AI, its challenges, and future directions, while providing a foundation for further exploration and innovation in this rapidly evolving field.