Sample Noise Impact on Active Learning
This work addresses the problem of noisy sample selection in active learning for researchers, but it is incremental as it builds on prior work with limited real-world impact.
The paper investigates how noisy sample selection affects active learning strategies, demonstrating that incorporating sample noise knowledge can significantly improve performance on synthetic tasks but only marginally on real-life use-cases.
This work explores the effect of noisy sample selection in active learning strategies. We show on both synthetic problems and real-life use-cases that knowledge of the sample noise can significantly improve the performance of active learning strategies. Building on prior work, we propose a robust sampler, Incremental Weighted K-Means that brings significant improvement on the synthetic tasks but only a marginal uplift on real-life ones. We hope that the questions raised in this paper are of interest to the community and could open new paths for active learning research.