Active Transfer Learning under Model Shift

Abstract

Transfer learning algorithms are used when one has sufficient training data for one supervised learning task (the source task) but only very limited training data for a second task (the target task) that is similar but not identical to the first. These algorithms use varying assumptions about the similarity between the tasks to carry information from the source to the target task. Common assumptions are that only certain specific marginal or conditional distributions have changed while all else remains the same. Alternatively, if one has only the target task, but also has the ability to choose a limited amount of additional training data to collect, then active learning algorithms are used to make choices which will most improve performance on the target task. These algorithms may be combined into active transfer learning, but previous efforts have had to apply the two methods in sequence or use restrictive transfer assumptions. We propose two transfer learning algorithms that allow changes in all marginal and conditional distributions but assume the changes are smooth in order to achieve transfer between the tasks. We then propose an active learning algorithm for the second method that yields a combined active transfer learning algorithm. We demonstrate the algorithms on synthetic functions and a real-world task on estimating the yield of vineyards from images of the grapes.