arXiv:2607.01089v1 Announce Type: cross Abstract: 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,
The proliferation of AI models across various applications necessitates more efficient data labeling, making new active learning techniques critical for managing costs and improving model robustness.
This development offers a potential breakthrough in reducing AI development costs and accelerating iterative model training, especially in data-rich but label-scarce environments.
Active learning methodologies can now incorporate data symmetries, leading to more efficient acquisition of informative samples and potentially higher quality models with less labeled data.
- · AI development firms
- · Data labeling services (who adapt)
- · R&D intensive sectors (e.g., healthcare, manufacturing)
- · Researchers in machine learning
- · Traditional, brute-force data labeling approaches
- · AI projects with limited data budgets using inefficient methods
Reduced data annotation costs and accelerated AI model development cycles across various industries.
Democratization of advanced AI capabilities due to lower data barrier to entry, particularly for smaller teams or less resource-rich organizations.
Enhanced AI performance in complex real-world scenarios where data symmetries are prevalent, leading to more robust and reliable AI systems.
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Read at arXiv cs.LG