INVESTIGATION OF THE MAIN ASPECTS OF THE GENERATIVE MODELS APPLICATIONS IN MACHINE LEARNING

Abstract

This paper examines the fundamental characteristics and applications of generative deep learning models in machine learning, with particular focus on their methodological frameworks and practical implementations. To identify and analyze the implementation and application characteristics of generative models, we conduct a systematic examination of four main types: autoregressive models, which have become foundational for large language models and sequential data processing; variational autoencoders, which utilize latent space constraints and probabilistic encoding; generative adversarial networks, which employ discriminator-based learning through competitive neural architectures; and diffusion models, which implement a noise-reduction approach through iterative refinement. Each model type presents distinct methodological solutions to the challenge of generating objects that approximate the probability distribution of a given dataset, offering unique advantages and considerations for different application scenarios. This analysis contributes to the understanding of how different generative architectures can be effectively utilized in various machine learning applications.

The research demonstrates the practical applications of these models across multiple domains, highlighting their impact on contemporary machine learning tasks. In computer vision and multimedia, they have proven effective for image synthesis, super-resolution enhancement, and video generation, contributing to significant advances in content creation and modification. Their application extends to healthcare, where they facilitate the generation of synthetic patient data while maintaining privacy requirements and adhering to ethical guidelines. The study highlights the particular utility of generative models in data augmentation scenarios, especially in fields where data collection faces practical or ethical limitations, such as medical imaging and specialized research domains. Special attention is given to the conditioning mechanism that enables natural language interaction with the generation process, which has led to significant advances in text-to-image and text-to-video applications.