Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science

Abstract

Deep learning techniques usually require a large quantity of training data and may be challenging for scarce datasets. The authors propose a framework that involves contrastive and transfer learning and reduces data requirements for training while keeping the prediction accuracy.