Xueying Zhan

Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios

2022 · arXiv (Cornell University)

Pool-based Active Learning (AL) has achieved great success in minimizing labeling cost by sequentially selecting informative unlabeled samples from a large unlabeled data pool and querying their labels from oracle/annotators. However, existing AL sampling strategies …