使用CKA与经验方法优化远程光电容积描记架构
Refining Remote Photoplethysmography Architectures using CKA and Empirical Methods
AI总结：
本文利用中心核对齐（CKA）分析不同深度rPPG模型的表示相似性与冗余，结合实证研究验证架构缺陷对性能的影响，提出以CKA为诊断工具优化rPPG架构的方法。
摘要：
模型架构优化是深度学习研究领域（如远程光电容积描记，rPPG）中的一项具有挑战性的任务。其中一个架构考量因素——模型深度，可能对最终性能产生显著影响。在层数多于必要数量的rPPG模型中，存在冗余，移除这些冗余可以加快训练速度并降低推理时的计算负载。而层数过少时，模型可能表现出次优的错误率。我们将中心核对齐（Centered Kernel Alignment，CKA）应用于一系列不同深度的rPPG架构，证明较浅的模型无法学习到与较深模型相同的表示，并且在达到一定深度之后，增加的冗余层并未显著提升功能。一项实证研究证实了使用CKA发现的架构缺陷如何影响性能，并且我们展示了CKA作为一种诊断工具如何用于优化rPPG架构。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
