The More the Merrier: The Effects of Data Volume and Transfer Learning on Creating Generalizable Deep Learning PAM Detectors
TL;DR
While trained models saw a drop in performance when tested on a new dataset, those pretrained on large diverse datasets outperformed limited target‐specific training even when deployment conditions were not represented during training, confirming training diversity improves transferability.
Abstract
Passive acoustic monitoring (PAM) generates large datasets that are costly to analyze manually, creating the need for accurate automated detection methods. While deep learning (DL) has shown promise for PAM‐related tasks, the transferability of trained models to unseen datasets remains a critical challenge. To address this, we evaluated four methodological approaches for maximizing the transferability of a DL sperm whale ( Physeter macrocephalus ) click detector: (1) cross‐environment evaluation (training on five datasets, testing on an unseen sixth), (2) limited target‐domain data training (model retraining using only a small, site‐specific dataset), (3) pretraining with random fine‐tuning , and (4) pretraining with active fine‐tuning (selecting fine‐tuning samples based on model uncertainty). While trained models saw a drop in performance when tested on a new dataset, those pretrained on large diverse datasets outperformed limited target‐specific training even when deployment conditions were not represented during training, confirming training diversity improves transferability. Fine‐tuning with 500 target recordings effectively mitigated performance drops when deploying a trained model to new datasets. Active fine‐tuning consistently outperformed all other approaches, although the improvement over random fine‐tuning was marginal. For new studies, we recommend using models pretrained on diverse datasets and fine‐tuning them with minimal target data.