Measuring Domain Shifts using Deep Learning Remote Photoplethysmography
  Model Similarity
Nathan Vance Patrick Flynn
Domain shift differences between training data for deep learning models and
the deployment context can result in severe performance issues for models which
fail to generalize. We study the domain shift problem under the context of
remote photoplethysmography (rPPG), a technique for video-based heart rate
inference. We propose metrics based on model similarity which may be used as a
measure of domain shift, and we demonstrate high correlation between these
metrics and empirical performance. One of the proposed metrics with viable
correlations, DS-diff, does not assume access to the ground truth of the target
domain, i.e. it may be applied to in-the-wild data. To that end, we investigate
a model selection problem in which ground truth results for the evaluation
domain is not known, demonstrating a 13.9% performance improvement over the
average case baseline.