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 how the architectural deficiencies discovered using CKA impact performance, and we show how CKA as a diagnostic can be used to refine rPPG architectures.

翻译：模型架构优化是远程光电容积描记法(rPPG)等深度学习研究领域中的一项挑战性任务。模型深度作为架构设计维度之一，会对最终性能产生显著影响。当rPPG模型因层数过度冗余时，其中存在的冗余结构可通过裁剪实现训练加速与推理计算负载降低；而层数不足则可能导致模型误差率非最优。本研究将中心核对齐(CKA)方法应用于不同深度的rPPG架构阵列，证明浅层模型无法习得与深层模型相同的表征，且当深度达到特定阈值后，新增层将呈现冗余性而无法显著提升功能。通过实证研究验证了CKA所揭示的架构缺陷对性能的影响机制，并展示了如何将该诊断工具用于优化rPPG架构。