Refining Remote Photoplethysmography Architectures using CKA and Empirical Methods

arXiv:2401.04801v2 Announce Type: replace
Abstract: 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 …

abstract architecture architectures arxiv cka consequences cs.cv deep learning fields performance research type