# Measuring Domain Shifts using Deep Learning Remote Photoplethysmography Model Similarity

arXiv: https://arxiv.org/abs/2404.08184 · HTML: https://mlsanity.com/paper/2404.08184/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 12 Apr 2024.

## Builds on (most cited first, 12 of 12)

- [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019)
- [Insights on representational similarity in neural networks with canonical correlation](https://mlsanity.com/paper/1806.05759/) (2018)
- [Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation](https://mlsanity.com/paper/1809.09478/) (2018)
- [Adaptive Risk Minimization: Learning to Adapt to Domain Shift](https://mlsanity.com/paper/2007.02931/) (2020)
- [Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement](https://mlsanity.com/paper/2006.03790/) (2020)
- [Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks](https://mlsanity.com/paper/1905.02419/) (2019)
- [MMPD: Multi-Domain Mobile Video Physiology Dataset](https://mlsanity.com/paper/2302.03840/) (2023)
- [Deconfounded Representation Similarity for Comparison of Neural Networks](https://mlsanity.com/paper/2202.00095/) (2022)
- [Deception Detection and Remote Physiological Monitoring: A Dataset and Baseline Experimental Results](https://mlsanity.com/paper/2106.06583/) (2021)
- [MSPM: A Multi-Site Physiological Monitoring Dataset for Remote Pulse, Respiration, and Blood Pressure Estimation](https://mlsanity.com/paper/2402.02224/) (2024)
- [Promoting Generalization in Cross-Dataset Remote Photoplethysmography](https://mlsanity.com/paper/2305.15199/) (2023)
- [Refining Remote Photoplethysmography Architectures using CKA and Empirical Methods](https://mlsanity.com/paper/2401.04801/) (2024)

## Built on by (most cited first, 2 of 2)

- [Domain-Adaptive Neural Posterior Estimation for Strong Gravitational Lens Analysis](https://mlsanity.com/paper/2410.16347/) (2024)
- [Prmpt2Adpt: Prompt-Based Zero-Shot Domain Adaptation for Resource-Constrained Environments](https://mlsanity.com/paper/2506.16994/) (2025)

## Abstract (arXiv)

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.