特征多样性在开放集识别与持续学习中作用的经验证据
Empirical Evidences for the Effects of Feature Diversity in Open Set Recognition and Continual Learning
AI总结：
本文通过实验证明增强特征多样性既能提升开放集样本识别能力，又能促进持续学习中旧知识保留与新数据整合，为相关方法设计提供依据。
摘要：
开放集识别（OSR）和持续学习是机器学习中的两个关键挑战，分别关注在推理时检测新类别以及更新模型以纳入新类别。尽管许多近期方法，尤其是针对OSR的方法，通过启发式地促进特征多样性来解决这些问题，但很少有研究直接考察特征多样性在应对这些问题中所起的作用。在这项工作中，我们提供了经验证据，表明增强特征多样性能够改善对开放集样本的识别。此外，增加特征多样性还有助于在持续学习中保留先前学习的数据并整合新数据。我们希望我们的发现能够启发对这些领域中实用方法和理论理解的进一步研究。
英文摘要：
Open set recognition (OSR) and continual learning are two critical challenges in machine learning, focusing respectively on detecting novel classes at inference time and updating models to incorporate the new classes. While many recent approaches have addressed these problems, particularly OSR, by heuristically promoting feature diversity, few studies have directly examined the role that feature diversity plays in tackling them. In this work, we provide empirical evidence that enhancing feature diversity improves the recognition of open set samples. Moreover, increased feature diversity also facilitates both the retention of previously learned data and the integration of new data in continual learning. We hope our findings can inspire further research into both practical methods and theoretical understanding in these domains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
