  While insects are the largest and most diverse group of terrestrial animals,
constituting ca. 80% of all known species, they are difficult to study due to
their small size and similarity between species. Conventional monitoring
techniques depend on time consuming trapping methods and tedious
microscope-based work by skilled experts in order to identify the caught insect
specimen at species, or even family level. Researchers and policy makers are in
urgent need of a scalable monitoring tool in order to conserve biodiversity and
secure human food production due to the rapid decline in insect numbers.
  In order to improve upon existing insect clustering methods, we propose an
adaptive variant of the variational autoencoder (VAE) which is capable of
clustering data by phylogenetic groups. The proposed dynamic beta-VAE
dynamically adapts the scaling of the reconstruction and regularization loss
terms (beta value) yielding useful latent representations of the input data. We
demonstrate the usefulness of the dynamic beta-VAE on optically recorded insect
signals from regions of southern Scandinavia to cluster unlabelled targets into
possible species. We also demonstrate improved clustering performance in a
semi-supervised setting using a small subset of labelled data. These
experimental results, in both unsupervised- and semi-supervised settings, with
the dynamic beta-VAE are promising and, in the near future, can be deployed to
monitor insects and conserve the rapidly declining insect biodiversity.
