  The deployment of machine learning solutions in real-world scenarios often
involves addressing the challenge of out-of-distribution (OOD) detection. While
significant efforts have been devoted to OOD detection in classical supervised
settings, the context of weakly supervised learning, particularly the Multiple
Instance Learning (MIL) framework, remains under-explored. In this study, we
tackle this challenge by adapting post-hoc OOD detection methods to the MIL
setting while introducing a novel benchmark specifically designed to assess OOD
detection performance in weakly supervised scenarios. Across extensive
experiments based on diverse public datasets, KNN emerges as the
best-performing method overall. However, it exhibits significant shortcomings
on some datasets, emphasizing the complexity of this under-explored and
challenging topic. Our findings shed light on the complex nature of OOD
detection under the MIL framework, emphasizing the importance of developing
novel, robust, and reliable methods that can generalize effectively in a weakly
supervised context. The code for the paper is available here:
https://github.com/loic-lb/OOD_MIL.
