Maximally Separated Active Learning

ECCV 2024 · Beyond Euclidean Workshop

Tejaswi Kasarla¹, Abhishek Jha¹, Faye Tervoort¹, Rita Cucchiara², Pascal Mettes¹

¹VIS Lab, University of Amsterdam²University of Modena and Reggio Emilia

Abstract

Active learning aims to select the most informative unlabeled samples for annotation. We propose Maximally Separated Active Learning, which applies the principle of maximum class separation — where class prototypes are arranged as an equiangular tight frame in feature space — as an inductive bias for sample selection. By querying samples that best support maximally separated representations, our approach achieves strong performance with fewer labeled examples across classification benchmarks.

BibTeX

@inproceedings{kasarla2024maximally,
  title={Maximally Separated Active Learning},
  author={Kasarla, Tejaswi and Jha, Abhishek and Tervoort, Faye and Cucchiara, Rita and Mettes, Pascal},
  booktitle={European Conference on Computer Vision Workshop},
  year={2024}
}