  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 might not work well in
out-of-distribution (OOD) data scenarios, where the unlabeled data pool
contains some data samples that do not belong to the classes of the target
task. Achieving good AL performance under OOD data scenarios is a challenging
task due to the natural conflict between AL sampling strategies and OOD sample
detection. AL selects data that are hard to be classified by the current basic
classifier (e.g., samples whose predicted class probabilities have high
entropy), while OOD samples tend to have more uniform predicted class
probabilities (i.e., high entropy) than in-distribution (ID) data. In this
paper, we propose a sampling scheme, Monte-Carlo Pareto Optimization for Active
Learning (POAL), which selects optimal subsets of unlabeled samples with fixed
batch size from the unlabeled data pool. We cast the AL sampling task as a
multi-objective optimization problem, and thus we utilize Pareto optimization
based on two conflicting objectives: (1) the normal AL data sampling scheme
(e.g., maximum entropy), and (2) the confidence of not being an OOD sample.
Experimental results show its effectiveness on both classical Machine Learning
(ML) and Deep Learning (DL) tasks.
