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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 totheir small size and similarity between species. Conventional monitoringtechniques depend on time consuming trapping methods and tediousmicroscope-based work by skilled experts in order to identify the caught insectspecimen at species, or even family level. Researchers and policy makers are inurgent need of a scalable monitoring tool in order to conserve biodiversity andsecure human food production due to the rapid decline in insect numbers.  In order to improve upon existing insect clustering methods, we propose anadaptive variant of the variational autoencoder (VAE) which is capable ofclustering data by phylogenetic groups. The proposed dynamic beta-VAEdynamically adapts the scaling of the reconstruction and regularization lossterms (beta value) yielding useful latent representations of the input data. Wedemonstrate the usefulness of the dynamic beta-VAE on optically recorded insectsignals from regions of southern Scandinavia to cluster unlabelled targets intopossible species. We also demonstrate improved clustering performance in asemi-supervised setting using a small subset of labelled data. Theseexperimental results, in both unsupervised- and semi-supervised settings, withthe dynamic beta-VAE are promising and, in the near future, can be deployed tomonitor insects and conserve the rapidly declining insect biodiversity.

Dynamic $\beta$-VAEs for quantifying biodiversity by clustering optically recorded insect signals