6 citations · 11 across the 5 of their papers we have counts for
5 papers
Deep Evidential Regression for Sparse Forest Height Estimation from Multimodal Satellite Imagery
Laura Bader, Muhammad Ammar Ahmed, Xiao Xiang Zhu +1
Accurate estimation of forest height from satellite imagery is essential for applications such as carbon accounting, biodiversity monitoring, and ecosystem management. While recent…
Revisiting Active Learning under (Human) Label Variation
Cornelia Gruber, Helen Alber, Bernd Bischl +3
Access to high-quality labeled data remains a limiting factor in applied supervised learning. While label variation (LV), i.e., differing labels for the same instance, is common, e…
Human-in-the-loop: Towards Label Embeddings for Measuring Classification Difficulty
Katharina Hechinger, Christoph Koller, Xiao Xiang Zhu +1
Uncertainty in machine learning models is a timely and vast field of research. In supervised learning, uncertainty can already occur in the first stage of the training process, the…
Categorising the World into Local Climate Zones -- Towards Quantifying Labelling Uncertainty for Machine Learning Models
Katharina Hechinger, Xiao Xiang Zhu, Göran Kauermann
Image classification is often prone to labelling uncertainty. To generate suitable training data, images are labelled according to evaluations of human experts. This can result in…
Going Beyond One-Hot Encoding in Classification: Can Human Uncertainty Improve Model Performance?
Christoph Koller, Göran Kauermann, Xiao Xiang Zhu
Technological and computational advances continuously drive forward the broad field of deep learning. In recent years, the derivation of quantities describing theuncertainty in the…