Distribution Aware Active Learning via Gaussian Mixtures

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

Keywords: Active Learning

TL;DR: We propose distribution aware active learning that resolve distribution discrepancy of feature presentations between labeled set and unlabeled set using Gaussin mixtures.

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.