Group-invariant Coresets for Data-efficient Active Learning
Active learning reduces labeling cost by querying the most informative unlabeled samples, but standard coreset methods ignore known data symmetries and can waste budget on transformed versions of the same instance. We propose GRINCO, a group-invariant coreset framework that performs acquisition in the quotient space induced by a transformation group, so that selection operates on orbits rather than raw samples. The method uses either canonical representatives or learned orbit-separating invariant embeddings to define practical quotient metrics, and combines quotient-space k-center selection with invariant training through an orbit-averaged loss. We further derive a generalization bound th...
| Authors | L. C. Ayres, J. C. M. Bermudez, S. J. M. de Almeida et al. |
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| Primary category | eess.IV |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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