Active Learning of Ordinal Embeddings: A User Study on Football Data. (arXiv:2207.12710v2 [cs.LG] UPDATED)

Humans innately measure distance between instances in an unlabeled dataset
using an unknown similarity function. Distance metrics can only serve as proxy
for similarity in information retrieval of similar instances. Learning a good
similarity function from human annotations improves the quality of retrievals.
This work uses deep metric learning to learn these user-defined similarity
functions from few annotations for a large football trajectory dataset. We
adapt an entropy-based active learning method with recent work from triplet
mining to collect easy-to-answer …

active learning arxiv data football ordinal study