Better Modelling Out-of-Distribution Regression on Distributed Acoustic Sensor Data Using Anchored Hidden State Mixup
This addresses the problem of model generalization under distribution shifts for researchers in sensor data analysis and OOD learning, though it appears incremental as it builds on existing mixup techniques.
The paper tackles out-of-distribution regression by introducing an anchored-based OOD Regression Mixup algorithm with a novel regularization penalty, achieving state-of-the-art performance on a new high-resolution Distributed Acoustic Sensor dataset and showing improved generalization on other datasets like Udacity and Rotation-MNIST.
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.