正则化能显著提升模型识别未知类的能力，值得重视。
Effects of Common Regularization Techniques on Open-Set Recognition
- 测试多种常见正则化方法对开集识别的影响
- 正则化可明显提高开集识别准确率，且与闭集精度相关
- 适用于需要识别未知样本的现实场景模型优化
近年来，开集识别（Open-Set Recognition）受到越来越多关注，该任务使分类模型在遇到训练集中未出现的新类别时能够识别为“未知”。这一能力对许多实际应用场景至关重要。由于现代神经网络训练普遍采用大量正则化以提升泛化能力，因此有必要考察正则化技术如何影响模型的开集识别性能。本文系统研究了常见正则化方法与开集识别性能之间的关系，实验不依赖特定开集检测算法，覆盖广泛数据集。结果表明，正则化方法可显著提升开集识别性能，并揭示了准确率与开集表现之间新的关联机制。
原文摘要 · Abstract (English)
In recent years there has been increasing interest in the field of Open-Set Recognition, which allows a classification model to identify inputs as "unknown" when it encounters an object or class not in the training set. This ability to flag unknown inputs is of vital importance to many real world classification applications. As almost all modern training methods for neural networks use extensive amounts of regularization for generalization, it is therefore important to examine how regularization techniques impact the ability of a model to perform Open-Set Recognition. In this work, we examine the relationship between common regularization techniques and Open-Set Recognition performance. Our experiments are agnostic to the specific open-set detection algorithm and examine the effects across a wide range of datasets. We show empirically that regularization methods can provide significant improvements to Open-Set Recognition performance, and we provide new insights into the relationship between accuracy and Open-Set performance.
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