  While insects are the largest and most diverse group of 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. Recent work has aimed
for a broader analysis using unsupervised clustering as a proxy for
conventional biodiversity measures, such as species richness and species
evenness, without actually identifying the species of the detected target.
  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.
