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

TL;DR

This paper introduces a novel anchored-based OOD regression method using manifold hidden state mixup, validated on a new DAS dataset and other regression datasets, achieving state-of-the-art generalization performance.

Contribution

Proposes a new anchored hidden state mixup regularization technique for OOD regression, and provides a high-resolution DAS dataset for benchmarking.

Findings

Achieves state-of-the-art OOD regression performance

Demonstrates improved generalization on multiple datasets

Provides a new DAS dataset for research benchmarking

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 paper 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 (DAS) dataset that is suitable for testing OOD regression modelling, 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, then show that our method achieves state-of-the-art performance. Lastly, 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.