Refining Remote Photoplethysmography Architectures using CKA and
  Empirical Methods
Nathan Vance Patrick Flynn
Model architecture refinement is a challenging task in deep learning research
fields such as remote photoplethysmography (rPPG). One architectural
consideration, the depth of the model, can have significant consequences on the
resulting performance. In rPPG models that are overprovisioned with more layers
than necessary, redundancies exist, the removal of which can result in faster
training and reduced computational load at inference time. With too few layers
the models may exhibit sub-optimal error rates. We apply Centered Kernel
Alignment (CKA) to an array of rPPG architectures of differing depths,
demonstrating that shallower models do not learn the same representations as
deeper models, and that after a certain depth, redundant layers are added
without significantly increased functionality. An empirical study confirms
these findings and shows how this method could be used to refine rPPG
architectures.