Active learning aims to develop label-efficient algorithms by querying the
most representative samples to be labeled by a human annotator. Current active
learning techniques either rely on model uncertainty to select the most
uncertain samples or use clustering or reconstruction to choose the most
diverse set of unlabeled examples. While uncertainty-based strategies are
susceptible to outliers, solely relying on sample diversity does not capture
the information available on the main task. In this work, we develop a
semi-supervised minimax entropy-based active learning algorithm that leverages
both uncertainty and diversity in an adversarial manner. Our model consists of
an entropy minimizing feature encoding network followed by an entropy
maximizing classification layer. This minimax formulation reduces the
distribution gap between the labeled/unlabeled data, while a discriminator is
simultaneously trained to distinguish the labeled/unlabeled data. The highest
entropy samples from the classifier that the discriminator predicts as
unlabeled are selected for labeling. We evaluate our method on various image
classification and semantic segmentation benchmark datasets and show superior
performance over the state-of-the-art methods.

该研究发展了一种基于半监督极小极大熵的主动学习算法，通过同时利用不确定性和多样性来选择最具代表性的样本，从而在图像分类和语义分割任务中表现优于现有的技术。