18 citations · 36 across the 16 of their papers we have counts for
5 papers · 1 filter
dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple Humans
Marek Herde, Denis Huseljic, Lukas Rauch +1
Human annotators typically provide annotated data for training machine learning models, such as neural networks. Yet, human annotations are subject to noise, impairing generalizati…
Towards Deep Active Learning in Avian Bioacoustics
Lukas Rauch, Denis Huseljic, Moritz Wirth +3
Passive acoustic monitoring (PAM) in avian bioacoustics enables cost-effective and extensive data collection with minimal disruption to natural habitats. Despite advancements in co…
Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension
Marek Herde, Lukas Lührs, Denis Huseljic +1
Training with noisy class labels impairs neural networks' generalization performance. In this context, mixup is a popular regularization technique to improve training robustness by…
Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image Classification
Denis Huseljic, Paul Hahn, Marek Herde +2
Deep active learning (AL) seeks to minimize the annotation costs for training deep neural networks. BAIT, a recently proposed AL strategy based on the Fisher Information, has demon…
BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics
Lukas Rauch, Raphael Schwinger, Moritz Wirth +8
Deep learning (DL) has greatly advanced audio classification, yet the field is limited by the scarcity of large-scale benchmark datasets that have propelled progress in other domai…