Open-world classification systems should discern out-of-distribution (OOD)
data whose labels deviate from those of in-distribution (ID) cases, motivating
recent studies in OOD detection. Advanced works, despite their promising
progress, may still fail in the open world, owing to the lack of knowledge
about unseen OOD data in advance. Although one can access auxiliary OOD data
(distinct from unseen ones) for model training, it remains to analyze how such
auxiliary data will work in the open world. To this end, we delve into such a
problem from a learning theory perspective, finding that the distribution
discrepancy between the auxiliary and the unseen real OOD data is the key to
affecting the open-world detection performance. Accordingly, we propose
Distributional-Augmented OOD Learning (DAL), alleviating the OOD distribution
discrepancy by crafting an OOD distribution set that contains all distributions
in a Wasserstein ball centered on the auxiliary OOD distribution. We justify
that the predictor trained over the worst OOD data in the ball can shrink the
OOD distribution discrepancy, thus improving the open-world detection
performance given only the auxiliary OOD data. We conduct extensive evaluations
across representative OOD detection setups, demonstrating the superiority of
our DAL over its advanced counterparts.

开放世界分类系统中，鉴别与内部分布不同的外部数据是一个重要问题，利用辅助外部数据进行模型训练可以改善开放世界检测性能。本文从学习理论的角度，通过构建一个包含辅助外部数据 Wasserstein 球中的全部分布的外部分布集，提出了分布增强的外部数据学习 (DAL) 方法，通过对球中的最差外部数据进行训练，缩小外部数据分布差异，从而提高开放世界检测性能。结果表明，DAL 在代表性的外部数据检测设置中具有优越性。