18 citations · 27 across the 14 of their papers we have counts for
4 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…
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…