Self-Supervised and Few-Shot Learning for Robust Bioaerosol Monitoring
This work addresses the challenge of deploying deep-learning models for real-time bioaerosol monitoring, which is important for people with allergies, by reducing the need for large annotated datasets, though it is incremental in applying existing techniques to this domain.
The paper tackled the problem of bioaerosol monitoring by combining self-supervised and few-shot learning to classify holographic images, using unlabelled data and few labelled examples, which improved identification and reduced adaptation effort.
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.