Pretraining on diverse data and fine-tuning with 500 target recordings lifted a sperm whale click-train detector's AUC from as low as 0.60 to 0.78–0.97 on unseen datasets
Starting from a previously trained temporal convolutional network sperm whale click-train detector, the study compared four transfer approaches across four sources (BAL, CS, ICE, MED)—cross-dataset baseline evaluation, training from scratch on only 500 target recordings, pretraining with random fine-tuning, and pretraining with active (uncertainty-based) fine-tuning—and found that pretrained models dropped in performance on unseen data but that fine-tuning with 500 target recordings effectively mitigated the drop, with active fine-tuning consistently outperforming the other approaches although its gain over random fine-tuning was marginal.