Emerging Applications of Generative: Techniques, Use Cases, and Challenges

Abstract

Generative models have gained significant attention in machine learning for their
ability to generate new data instances resembling a given distribution. This paper surveys the
emerging applications of generative models before 2013, focusing on areas such as image
synthesis, data augmentation, natural language processing, and unsupervised learning. By
reviewing early techniques, including Gaussian Mixture Models (GMMs), Restricted Boltzmann
Machines (RBMs), and Deep Belief Networks (DBNs), we explore their potential applications
and the challenges that arose in their adoption. Despite limitations in scalability and complexity,
generative models laid the foundation for future advancements in generative adversarial
networks (GANs) and variational autoencoders (VAEs).

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