  Active Learning (AL) aims to reduce the labeling burden by interactively
selecting the most informative samples from a pool of unlabeled data. While
there has been extensive research on improving AL query methods in recent
years, some studies have questioned the effectiveness of AL compared to
emerging paradigms such as semi-supervised (Semi-SL) and self-supervised
learning (Self-SL), or a simple optimization of classifier configurations.
Thus, today's AL literature presents an inconsistent and contradictory
landscape, leaving practitioners uncertain about whether and how to use AL in
their tasks. In this work, we make the case that this inconsistency arises from
a lack of systematic and realistic evaluation of AL methods. Specifically, we
identify five key pitfalls in the current literature that reflect the delicate
considerations required for AL evaluation. Further, we present an evaluation
framework that overcomes these pitfalls and thus enables meaningful statements
about the performance of AL methods. To demonstrate the relevance of our
protocol, we present a large-scale empirical study and benchmark for image
classification spanning various data sets, query methods, AL settings, and
training paradigms. Our findings clarify the inconsistent picture in the
literature and enable us to give hands-on recommendations for practitioners.
The benchmark is hosted at https://github.com/IML-DKFZ/realistic-al .
