Machine Learning in Montpellier, Theory & Practice – Ilyass Moummad (Inria)
Biodiversity monitoring has become increasingly popular as a way to better understand and assess the health of surrounding ecosystems. Sound, in particular, plays a crucial role in biodiversity monitoring, as many species rely on vocalizations to communicate and navigate their environment. Recent advancements in deep learning have improved our ability to automatically detect and classify sounds. However deep learning often require large annotated datasets, which can be costly and time-consuming for manual labelling, and difficult to obtain for rare or endangered species. To address these challenges, we explore the potential of transfer learning, where a feature extractor trained on one task is adapted to a new task. Specifically, we investigate invariant learning as a pre-training strategy designed to produce representations that are invariant to predefined transformations. In the unlabelled setting, this involves mapping a data example and its transformed counterpart to similar latent representations (transformation invariance), whereas in the labelled setting, examples sharing the same annotations are mapped to similar representations (label invariance). We then apply these learned representations to downstream tasks, focusing on the detection and classification of animal vocalizations in few-shot scenarios.
