Active learning focuses on choosing a subset of unlabeled data to be labeled. However, most such methods assume that a large subset of the data can be annotated. We are interested in low-budget active learning where only a small subset (e.g., 0.2% of ImageNet) can be annotated. Instead of proposing a new query strategy to iteratively sample batches of unlabeled data given an initial pool, we learn rich features by an off-the-shelf self-supervised learning method only once and then study the effectiveness of different sampling strategies given a low budget on a variety of datasets as well as ImageNet dataset. We show that although the state-of-the-art active learning methods work well given a large budget of data labeling, a simple k-means clustering algorithm can outperform them on low budgets. We believe this method can be used as a simple baseline for low-budget active learning on image classification. Code is available at: https://github.com/UCDvision/low-budget-al

本篇研究利用自我监督学习方法提取丰富特征，并研究了针对低标记预算下不同采样策略的有效性，表明虽然主流主动学习方法在高标记预算下表现出色，但简单的K均值聚类方法在低预算下可以胜任，并可作为图像分类低预算主动学习的基线方法。

低预算主动学习的简单基线