Sample Noise Impact on Active Learning

Code

• github.com/dataiku-research/sample_noise_impact_on_active_learning
  OfficialIn papertf★ 3
• github.com/dataiku-research/paper_ial_2021
  OfficialIn papertf★ 3

Abstract

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.

Tasks

Reproductions