  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.
