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.

翻译：开放集识别是机器学习中的一个关键方面，旨在解决推理过程中检测新类别的挑战。在深度学习领域，基于封闭数据集训练的神经分类器通常难以识别新类别，导致错误预测。为解决此问题，研究者提出了多种启发式方法，使模型能够通过表达“我不知道”来体现不确定性。然而，文献中仍存在空白，因为这些方法的底层机制仍未得到充分探索。本文从特征多样性的角度对开放集识别方法进行了分析，发现学习多样判别特征与提升开放集识别性能之间存在显著相关性。基于此洞察，我们提出了一种利用特征多样性优势的新型开放集识别方法。通过在标准开放集识别测试平台上的严格评估，我们的方法被证明优于当前最先进的方法，实现了显著的性能提升。