Title: Human experts vs. machines in taxa recognition,
Abstract: The step of expert taxa recognition currently slows down the response time of
many bioassessments. Shifting to quicker and cheaper state-of-the-art machine
learning approaches is still met with expert scepticism towards the ability and
logic of machines. In our study, we investigate both the differences in
accuracy and in the identification logic of taxonomic experts and machines. We
propose a systematic approach utilizing deep Convolutional Neural Nets with the
transfer learning paradigm and extensively evaluate it over a multi-label and
multi-pose taxonomic dataset specifically created for this comparison. We also
study the prediction accuracy on different ranks of taxonomic hierarchy in
detail. Our results revealed that human experts using actual specimens yield
the lowest classification error. However, our proposed, much faster, automated
approach using deep Convolutional Neural Nets comes very close to human
accuracy. Contrary to previous findings in the literature, we find that
machines following a typical flat classification approach commonly used in
machine learning performs better than forcing machines to adopt a hierarchical,
local per parent node approach used by human taxonomic experts. Finally, we
publicly share our unique dataset to serve as a public benchmark dataset in
this field.