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（OSR）是机器学习的关键领域，致力于解决在推理过程中检测新类别的挑战。在深度学习范畴内，基于封闭数据集训练的神经分类器通常难以识别新类别，从而导致错误预测。针对此问题，学界已提出多种启发式方法，使模型能够通过声明“未知”来表达不确定性。然而，现有研究仍存在空白，对这些方法内在机制的探索尚显不足。本文对开放集识别方法展开分析，重点关注特征多样性维度。研究发现，学习多样化的判别性特征与提升OSR性能存在显著关联。基于此洞见，我们提出一种新颖的OSR方法，充分利用特征多样性的优势。通过在标准OSR测试平台上的严格评估，本方法的有效性得到验证，其性能较现有最优方法实现显著提升。