Regularized Domain Adaptation for Estimation Tasks in Partially Observed Target Domains

Varun Kelkar, H. S. Melihcan Erol, Muhammad Aneeq Uz Zaman, Omer Tanovic, Ravi Kiran Raman

Abstract: The performance of machine learning algorithms is limited by the availability of training data. Transfer learning can alleviate this limitation by adapting models trained in data-rich domains to a data-sparse domain. In this work, we propose a method for data augmentation in a partially sampled datasparse target domain by transporting structural insights on data from a data-rich source domain. As an example, we show how this approach can improve the performance of a battery core temperature estimation based on electrochemical impedance spectroscopy (EIS) measurements, when only limited training data is available for new battery chemistries and form factors.

External IDs:dblp:conf/icassp/KelkarEZTR25