Discrete vs. Continuous Trade-offs for Generative Models

• URL: http://arxiv.org/abs/2412.19114v1
• Date: Thu, 26 Dec 2024 08:14:27 GMT
• Title: Discrete vs. Continuous Trade-offs for Generative Models
• Authors: Jathin Korrapati, Tanish Baranwal, Rahul Shah,
• Abstract summary: This work explores the theoretical and practical foundations of denoising diffusion probabilistic models (DDPMs) DDPMs and score-based generative models, which leverage processes and Brownian motion to model complex data distributions.
• Score: 0.0
• License:
• Abstract: This work explores the theoretical and practical foundations of denoising diffusion probabilistic models (DDPMs) and score-based generative models, which leverage stochastic processes and Brownian motion to model complex data distributions. These models employ forward and reverse diffusion processes defined through stochastic differential equations (SDEs) to iteratively add and remove noise, enabling high-quality data generation. By analyzing the performance bounds of these models, we demonstrate how score estimation errors propagate through the reverse process and bound the total variation distance using discrete Girsanov transformations, Pinsker's inequality, and the data processing inequality (DPI) for an information theoretic lens.

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