Explainability of TFCDiff’s noise-learning mechanism and generalization behavior

Establish an interpretable and mechanistic explanation for how the TFCDiff time–frequency complementary conditional diffusion model learns the noise distribution from electrocardiogram signals, and determine why diffusion models, including TFCDiff, exhibit superior cross-dataset generalization compared with other denoising methods even when they underperform on intra-dataset samples.

Background

TFCDiff is a conditional diffusion model defined in the discrete cosine transform domain with a Temporal Feature Enhancement Mechanism, designed to denoise 10‑second ECG segments under flexible random mixed noise. It achieves state-of-the-art performance on synthesized data and strong generalization on the SimEMG dataset.

Despite these results, the authors explicitly acknowledge that the mechanism by which TFCDiff learns noise distributions and achieves superior generalization is not understood. They emphasize that the model’s internal decision process remains a ‘black box’, raising interpretability concerns for clinical deployment and motivating explainability-focused research.