GOAT-AL: Pseudo Neural Collapse Guides Adaptive Coverage for All-Budget Active Learning

A central challenge in deep Active Learning is that optimal selection strategies depend on the labeling budget, shifting from coverage in low-budget regimes to uncertainty in high-budget regimes. Existing methods attempt to bridge this gap via heuristic switching or interpolation, but lack a principled mechanism to determine when such transitions should occur. We address this with pseudo-CDNV (pCDNV), a label-free proxy for neural collapse that tracks representation maturity. We show that pCDNV exhibits a characteristic peak that reliably signals when learned representations become suitable for uncertainty-based selection. Building on this insight, we propose Geometry-Oriented Adaptive Targeting (GOAT-AL), a unified query strategy that operates across all budget regimes. Rather than switching objectives, GOAT-AL maintains a single coverage-based objective while adapting the underlying feature space from self-supervised to task-aligned representations. Experiments on CIFAR-10, CIFAR-100, and TinyImageNet demonstrate that GOAT-AL consistently matches or outperforms state-of-the-art methods across low-, mid-, and high-budget regimes.