Synthetic Data Augmentation by Diffusion Probabilistic Models to Enhance Weed Recognition

Chen, Dong; Qi, Xinda; Zheng, Yu; Lu, Yuzhen; Huang, Yanbo; Li, Zhaojian

doi:10.2139/ssrn.4414966

Cited by 1 publication

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“…Related Work. A concurrent work by Zheng et al [ 20 ] presents an empirical study of a truncated diffusion process but lacks a rigorous analysis and a clear justification for the proposed approach. Recent attempts by Lee et al [ 9 ] to optimize

, or the proposal to do so [ 21 ], have been studied in different contexts.…”

Section: Introductionmentioning

confidence: 99%

How Much Is Enough? A Study on Diffusion Times in Score-Based Generative Models

Franzese

Rossi

Yang

et al. 2023

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Score-based diffusion models are a class of generative models whose dynamics is described by stochastic differential equations that map noise into data. While recent works have started to lay down a theoretical foundation for these models, a detailed understanding of the role of the diffusion time T is still lacking. Current best practice advocates for a large T to ensure that the forward dynamics brings the diffusion sufficiently close to a known and simple noise distribution; however, a smaller value of T should be preferred for a better approximation of the score-matching objective and higher computational efficiency. Starting from a variational interpretation of diffusion models, in this work we quantify this trade-off and suggest a new method to improve quality and efficiency of both training and sampling, by adopting smaller diffusion times. Indeed, we show how an auxiliary model can be used to bridge the gap between the ideal and the simulated forward dynamics, followed by a standard reverse diffusion process. Empirical results support our analysis; for image data, our method is competitive with regard to the state of the art, according to standard sample quality metrics and log-likelihood.

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, or the proposal to do so [ 21 ], have been studied in different contexts.…”

Section: Introductionmentioning

confidence: 99%