Better Modeling Out-of-Distribution Regression on Distributed Acoustic Sensor Data Using Anchored Hidden State Mixup

Abstract

Generalizing the application of machine learning models to situations where the statistical distribution of training and test data are different has been a complex problem. Our contributions in this article are threefold: 1) we introduce an anchored-based out-of-distribution (OOD) Regression Mixup algorithm, leveraging manifold hidden state mixup and observation similarities to form a novel regularization penalty; 2) we provide a first of its kind high-resolution distributed acoustic sensor dataset that is suitable for testing OOD regression modeling, allowing other researchers to benchmark progress in this area; and 3) we demonstrate with an extensive evaluation the generalization performance of the proposed method against existing approaches and then show that our method achieves state-of-the-art performance. We also demonstrate a wider applicability of the proposed method by exhibiting improved generalization performances on other types of regression datasets, including Udacity and Rotation-MNIST datasets.