提升特征多样性可增强模型对未知类的识别与持续学习能力。
Empirical Evidences for the Effects of Feature Diversity in Open Set Recognition and Continual Learning
- 通过实证验证特征多样性有助于识别未知类别。
- 特征多样性提升旧知识保留与新知识融合效果。
- 为开放集识别与持续学习提供可借鉴的实践方向。
开放集识别（OSR）和持续学习是机器学习中的两个关键挑战，分别关注推理时检测新类别以及更新模型以融入新类别。尽管近年来许多方法通过启发式手段提升特征多样性来应对这些问题，尤其是开放集识别，但很少有研究直接考察特征多样性在解决这些问题中的作用。本文提供了实证证据，表明增强特征多样性能够提升对开放集样本的识别性能；同时，更高的特征多样性也有助于在持续学习中更好地保留已有知识并有效整合新数据。我们的发现旨在推动该领域在实践方法与理论理解方面的进一步研究。
原文摘要 · Abstract (English)
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.
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