  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 labeling budget on a variety of datasets including
ImageNet. We show that although the state-of-the-art active learning methods
work well given a large labeling budget, 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
