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Studying marine plankton is critical to assessing the health of the world's oceans. To sample these important populations, oceanographers are increasingly using specially engineered in situ digital imaging systems that produce very large data sets. Most automated annotation efforts have considered data from individual systems in isolation. This is predicated on the assumption that the images from each system are so different that there would be little benefit to considering out-of-domain data. Meanwhile, in the computer vision community, much effort has been dedicated to understanding how using out-of-domain images can improve the performance of machine classifiers. In this paper, we leverage these advances to evaluate how well weights transfer between Convolutional Neural Networks (CNNs) trained on data from two radically different plankton imaging systems. We also examine the utility of CNNs as …