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cs.LG2023
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…
stat.AP2023★ 6 cited
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…