Toward Solving Domain Adaptation with Limited Source Labeled Data

The success of domain adaptation relies on highquality labeled data from the source domain, which is a luxury setup for applied machine learning problems. This article investigates a particular challenge: the source labeled data are neither plentiful nor sufficiently representative. We studied the challenge of limited data with an industrial application, i.e., forklift truck activity recognition. The task is to develop data-driven methods to recognize forklift usage performed in different warehouses with a large scale of signals collected from the onboard sensors. The preliminary results show that using pseudo-labeled data from the source domain can significantly improve classification performance on the target domain in some tasks. As the realworld problems are much more complex than typical research settings, it is not clearly understood in what circumstance the improvement may occur. Therefore, we provided discussions regarding this phenomenon and shared several inspirations on the difficulty of understanding and debugging domain adaptation problems in practice.