  We apply deep metric learning for the first time to the problem of
classifying planktic foraminifer shells on microscopic images. This species
recognition task is an important information source and scientific pillar for
reconstructing past climates. All foraminifer CNN recognition pipelines in the
literature produce black-box classifiers that lack visualization options for
human experts and cannot be applied to open-set problems. Here, we benchmark
metric learning against these pipelines, produce the first scientific
visualization of the phenotypic planktic foraminifer morphology space, and
demonstrate that metric learning can be used to cluster species unseen during
training. We show that metric learning outperforms all published CNN-based
state-of-the-art benchmarks in this domain. We evaluate our approach on the
34,640 expert-annotated images of the Endless Forams public library of 35
modern planktic foraminifera species. Our results on this data show leading 92%
accuracy (at 0.84 F1-score) in reproducing expert labels on withheld test data,
and 66.5% accuracy (at 0.70 F1-score) when clustering species never encountered
in training. We conclude that metric learning is highly effective for this
domain and serves as an important tool towards expert-in-the-loop automation of
microfossil identification. Keycode, network weights, and data splits are
published with this paper for full reproducibility.
