A TECHNICAL COMPARISON OF GANS, VARIATIONAL AUTOENCODERS, AND DIFFUSION MODELS IN MULTIMODAL CONTENT GENERATION

Basu Dev Shivhare; Radha Raman Chandan

Basu Dev Shivhare Professor, School of Computer Science & Engineering, Galgotias University, Greater Noida, India
Radha Raman Chandan Professor, Department of Computer Science, School of Management Sciences (SMS), Varanasi, India

Keywords: Generative Adversarial Networks, Variational Autoencoders, Diffusion Models, Generative Modelling, Sample Fidelity, Training Stability, Controllable Generation, Multimodal Generation, Latent Space, Denoising.

Abstract

In this study, we compare and contrast Generative Adversarial Networks, Variational Autoencoders and Diffusion Models in three central evaluation metrics: sample fidelity, controllability and training stability. It will cover the adversarial minimax formulation that has been the foundation for Generative Adversarial Networks, the training instabilities such as mode collapse which have historically hindered their reliability and the architectural and loss function modifications that have been made to improve the issues associated with such instabilities.

Based on the analysis, Diffusion Models currently have the best trade-off between sample fidelity and conditional controllability, with high inference latency but low training instability, Variational Autoencoders have the best sample fidelity and a stable training process with an interpretable latent space but lower inference speed, and Generative Adversarial Networks have the lowest training instability and inference speed but a less stable training process due to persistent training instability issues.