A Simple Baseline for Low-Budget Active Learning. (arXiv:2110.12033v2 [cs.CV] UPDATED)

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 …

active learning arxiv budget cv learning