Distribution Aware Active Learning via Gaussian Mixtures

Humaira Kousar, Younghyun Park, Dong-Jun Han, Jungwuk Park, Wonjeong Choi

Abstract:

In this paper, we propose a distribution-aware active learning strategy that captures
and mitigates the distribution discrepancy between the labeled and unlabeled sets
to cope with overfitting. By taking advantage of gaussian mixture models (GMM)
and Wasserstein distance, we first design a distribution-aware training strategy to
improve the model performance. Then, we introduce a hybrid informativeness
metric for active learning which considers both likelihood-based and model-based
information simultaneously. Experimental results on four different datasets show
the effectiveness of our method against existing active learning baselines.