  Real-time bioaerosol monitoring is improving the quality of life for people
affected by allergies, but it often relies on deep-learning models which pose
challenges for widespread adoption. These models are typically trained in a
supervised fashion and require considerable effort to produce large amounts of
annotated data, an effort that must be repeated for new particles, geographical
regions, or measurement systems. In this work, we show that self-supervised
learning and few-shot learning can be combined to classify holographic images
of bioaerosol particles using a large collection of unlabelled data and only a
few examples for each particle type. We first demonstrate that self-supervision
on pictures of unidentified particles from ambient air measurements enhances
identification even when labelled data is abundant. Most importantly, it
greatly improves few-shot classification when only a handful of labelled images
are available. Our findings suggest that real-time bioaerosol monitoring
workflows can be substantially optimized, and the effort required to adapt
models for different situations considerably reduced.
