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.

我们通过对开放集识别方法进行分析，重点关注特征多样性方面，揭示了学习多样化辨别特征与提升开放集识别性能之间的显著相关性，并基于此洞见提出了一种利用特征多样性优势的新型开放集识别方法，通过在标准的开放集识别测试平台上进行严格评估，证明我们的方法大幅优于现有方法。