Criteria for Selecting Embeddings to Drive Active Learning

Establish principled criteria for selecting an embedding representation to drive active learning in biodiversity monitoring, given that downstream performance is sensitive to the choice of embedding.

Background

The review describes a growing use of pretrained encoders and embeddings as the basis for active learning in biodiversity monitoring. In this setting, active learning selects an informative subset of an unlabelled pool for annotation, while the encoder may be frozen or jointly fine-tuned with a classification head.

Because downstream performance depends on the embedding representation, choosing an embedding is itself an important methodological decision. The review states that principled criteria for making this choice have not yet been established, identifying the issue as an open question in the context of adapting pretrained models for biodiversity monitoring.