Distribution Aware Active Learning
This work addresses the issue of inefficient sample selection in active learning for machine learning practitioners, though it is incremental as it builds on existing query criteria.
The paper tackles the problem of active learning being misled by outliers by proposing a distribution-aware query criterion that uses a probabilistic generative model as a teacher to incorporate dataset structure, resulting in improved robustness and performance in toy and real examples.
Discriminative learning machines often need a large set of labeled samples for training. Active learning (AL) settings assume that the learner has the freedom to ask an oracle to label its desired samples. Traditional AL algorithms heuristically choose query samples about which the current learner is uncertain. This strategy does not make good use of the structure of the dataset at hand and is prone to be misguided by outliers. To alleviate this problem, we propose to distill the structural information into a probabilistic generative model which acts as a \emph{teacher} in our model. The active \emph{learner} uses this information effectively at each cycle of active learning. The proposed method is generic and does not depend on the type of learner and teacher. We then suggest a query criterion for active learning that is aware of distribution of data and is more robust against outliers. Our method can be combined readily with several other query criteria for active learning. We provide the formulation and empirically show our idea via toy and real examples.