  A desirable data selection algorithm can efficiently choose the most
informative samples to maximize the utility of limited annotation budgets.
However, current approaches, represented by active learning methods, typically
follow a cumbersome pipeline that iterates the time-consuming model training
and batch data selection repeatedly. In this paper, we challenge this status
quo by designing a distinct data selection pipeline that utilizes existing
general-purpose models to select data from various datasets with a single-pass
inference without the need for additional training or supervision. A novel free
data selection (FreeSel) method is proposed following this new pipeline.
Specifically, we define semantic patterns extracted from inter-mediate features
of the general-purpose model to capture subtle local information in each image.
We then enable the selection of all data samples in a single pass through
distance-based sampling at the fine-grained semantic pattern level. FreeSel
bypasses the heavy batch selection process, achieving a significant improvement
in efficiency and being 530x faster than existing active learning methods.
Extensive experiments verify the effectiveness of FreeSel on various computer
vision tasks. Our code is available at https://github.com/yichen928/FreeSel.
