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F. Kubiczek, S. Patzke, and J. Thiem, "Generating Synthetic Spectral Data using Conditional DDPM," in 33rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2025, pp. 615-620.
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This study investigates the efficiency and effectiveness of Denoising Diffusion Probabilistic Models (DDPM) for generating synthetic spectral data. A modified DDPM was implemented and evaluated in comparison to a previously established model. Both models were trained with and without Classifier-Free Guidance (CFG). In addition, training duration and sample generation are compared. The results demonstrate that the synthetic spectral data exhibits a high degree of alignment with the training data, with only minor deviations. Furthermore, the influence of CFG on the generation process is evident. The findings indicate that the modified DDPM performs better on the given data.