Multi-layer attentive probing outperforms last-layer linear probing for transferring audio representations to bioacoustic tasks, indicating that standard evaluation setups may underestimate model quality.
Perch 2.0: The bittern lesson for bioacoustics
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Task vector arithmetic on near-orthogonal bioacoustic models allows composing multi-taxa classifiers without data sharing, with asymmetric accuracy gains for underrepresented taxa.
In moderate-sized fine-grained bioacoustics, pretraining scale of masked autoencoders on diverse general audio dominates over domain-specific objectives or data curation for transfer performance.
Active learning evaluation in ecology should be transductive rather than inductive, with a hybrid stopping rule that combines prediction and discovery metrics to better recover long-tail classes.
citing papers explorer
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Multi-layer attentive probing improves transfer of audio representations for bioacoustics
Multi-layer attentive probing outperforms last-layer linear probing for transferring audio representations to bioacoustic tasks, indicating that standard evaluation setups may underestimate model quality.
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Ecologically-Constrained Task Arithmetic for Multi-Taxa Bioacoustic Classifiers Without Shared Data
Task vector arithmetic on near-orthogonal bioacoustic models allows composing multi-taxa classifiers without data sharing, with asymmetric accuracy gains for underrepresented taxa.
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Masked Autoencoders with Limited Data: Does It Work? A Fine-Grained Bioacoustics Case Study
In moderate-sized fine-grained bioacoustics, pretraining scale of masked autoencoders on diverse general audio dominates over domain-specific objectives or data curation for transfer performance.
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Finding Needles in the Haystack: Transductive Active Labeling in Ecology
Active learning evaluation in ecology should be transductive rather than inductive, with a hybrid stopping rule that combines prediction and discovery metrics to better recover long-tail classes.