Generalize attribution behavior beyond RhythmFormer

Investigate whether the attribution behavior observed for RhythmFormer also appears in other neural-network architectures.

Background

The study evaluates four attribution methods exclusively on RhythmFormer models trained for remote photoplethysmography. Although the experiments compare conditions, datasets, and recording challenges, they do not test whether the observed relationships among skin coverage, SaCo, and model performance persist for other model architectures. Consequently, the external validity of the attribution findings beyond RhythmFormer remains unresolved.

References

Every statement here is about RhythmFormer, and whether the same attribution behaviour appears in another architecture is untested.

— Cross-Dataset Transfer and Reliability of Explainable Artificial Intelligence for RhythmFormer Remote Photoplethysmography  (2609.03663 - Chen et al., 3 Sep 2026) in Section 5.6, “Reproducibility, limitations, and future work”