Lamtyugina, A., Behera, A. K., Nandy, A., Floyd, C., & Vaikuntanathan, S. (2024). Score-based generative diffusion with "active" correlated noise sources. arXiv preprint arXiv:2411.07233.
This research paper explores the potential of incorporating time-correlated noise, inspired by active matter systems, into the forward diffusion process of score-based generative models to enhance their generative capabilities.
The authors develop an "active" diffusion process by introducing exponentially time-correlated noise into the forward dynamics of traditional score-based models. They derive the reverse diffusion process for this active scheme and compare its performance to the standard "passive" diffusion approach using various 2D toy models (Gaussian mixtures, overlapping Swiss rolls, alanine dipeptide Ramachandran plots) and high-dimensional Ising model simulations. The score functions are either analytically derived or learned using multi-layer perceptron (MLP) neural networks.
The introduction of active, time-correlated noise presents a promising avenue for improving the training and sampling efficiency of score-based generative diffusion models. This approach offers a new set of tunable hyperparameters, adding flexibility and control over the diffusion process.
This research contributes to the growing field of score-based generative modeling by introducing a novel, physics-inspired approach to enhance performance. It highlights the potential of leveraging insights from physical systems to advance machine learning techniques.
While the paper provides compelling numerical evidence, a comprehensive theoretical framework explaining the observed improvements with active diffusion is still needed. Further investigation into the optimal choice of correlated noise types and exploration of other physics-inspired diffusion processes are promising directions for future research.
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by Alexandra La... at arxiv.org 11-12-2024
https://arxiv.org/pdf/2411.07233.pdfDeeper Inquiries