  Open set recognition (OSR) is a critical aspect of machine learning,
addressing the challenge of detecting novel classes during inference. Within
the realm of deep learning, neural classifiers trained on a closed set of data
typically struggle to identify novel classes, leading to erroneous predictions.
To address this issue, various heuristic methods have been proposed, allowing
models to express uncertainty by stating "I don't know." However, a gap in the
literature remains, as there has been limited exploration of the underlying
mechanisms of these methods. In this paper, we conduct an analysis of open set
recognition methods, focusing on the aspect of feature diversity. Our research
reveals a significant correlation between learning diverse discriminative
features and enhancing OSR performance. Building on this insight, we propose a
novel OSR approach that leverages the advantages of feature diversity. The
efficacy of our method is substantiated through rigorous evaluation on a
standard OSR testbench, demonstrating a substantial improvement over
state-of-the-art methods.
