  Efficiency and trustworthiness are two eternal pursuits when applying deep
learning in real-world applications. With regard to efficiency, dataset
distillation (DD) endeavors to reduce training costs by distilling the large
dataset into a tiny synthetic dataset. However, existing methods merely
concentrate on in-distribution (InD) classification in a closed-world setting,
disregarding out-of-distribution (OOD) samples. On the other hand, OOD
detection aims to enhance models' trustworthiness, which is always
inefficiently achieved in full-data settings. For the first time, we
simultaneously consider both issues and propose a novel paradigm called
Trustworthy Dataset Distillation (TrustDD). By distilling both InD samples and
outliers, the condensed datasets are capable to train models competent in both
InD classification and OOD detection. To alleviate the requirement of real
outlier data and make OOD detection more practical, we further propose to
corrupt InD samples to generate pseudo-outliers and introduce Pseudo-Outlier
Exposure (POE). Comprehensive experiments on various settings demonstrate the
effectiveness of TrustDD, and the proposed POE surpasses state-of-the-art
method Outlier Exposure (OE). Compared with the preceding DD, TrustDD is more
trustworthy and applicable to real open-world scenarios. Our code will be
publicly available.
