  Transfer learning is a widely used method to build high performing computer
vision models. In this paper, we study the efficacy of transfer learning by
examining how the choice of data impacts performance. We find that more
pre-training data does not always help, and transfer performance depends on a
judicious choice of pre-training data. These findings are important given the
continued increase in dataset sizes. We further propose domain adaptive
transfer learning, a simple and effective pre-training method using importance
weights computed based on the target dataset. Our method to compute importance
weights follow from ideas in domain adaptation, and we show a novel application
to transfer learning. Our methods achieve state-of-the-art results on multiple
fine-grained classification datasets and are well-suited for use in practice.
