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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.

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