Authors: Stanislas Strasman, Antonio Ocello, Claire Boyer, Sylvain Le Corff, Vincent Lemaire

Published on: February 07, 2024

Impact Score: 8.15

Arxiv code: Arxiv:2402.04650

Summary

• What is new: The paper presents a novel approach to score-based generative models (SGMs) by introducing a method to optimize the noise schedule in a time-dependent manner, improving over existing time-homogeneous methods.
• Why this is important: Existing SGMs use a constant noise schedule which may not be optimal for accurately estimating the target data distribution.
• What the research proposes: The paper proposes an algorithm to automatically adjust the noise schedule in a time-dependent way, aimed at minimizing the divergence between the target and estimated distributions.
• Results: The optimized noise schedule demonstrated superior performance in minimizing the KL divergence and Wasserstein distance between the estimated and target distributions compared to standard choices.

Technical Details

Technological frameworks used: Score-based generative models (SGMs)

Models used: KL divergence, Wasserstein distance models

Data used: Noise-perturbed samples

Potential Impact

Data generation and synthesis, AI model training platforms, companies in the data analytics, and machine learning sectors.

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