Study Examines Uncertainty Sampling Performance Under Noisy Labels

2026-08-27

New research investigates whether uncertainty sampling in active learning falters due to acquiring more corrupted labels or errors concentrating in challenging data regions. The study compares margin-based uncertainty sampling with random sampling across various noise levels and annotation budgets.

Source: arXiv · cs.LG

Reported by VERA Newswire.