Modern neural networks are known to give overconfident prediction for
out-of-distribution inputs when deployed in the open world. It is common
practice to leverage a surrogate outlier dataset to regularize the model during
training, and recent studies emphasize the role of uncertainty in designing the
sampling strategy for outlier dataset. However, the OOD samples selected solely
based on predictive uncertainty can be biased towards certain types, which may
fail to capture the full outlier distribution. In this work, we empirically
show that diversity is critical in sampling outliers for OOD detection
performance. Motivated by the observation, we propose a straightforward and
novel sampling strategy named DOS (Diverse Outlier Sampling) to select diverse
and informative outliers. Specifically, we cluster the normalized features at
each iteration, and the most informative outlier from each cluster is selected
for model training with absent category loss. With DOS, the sampled outliers
efficiently shape a globally compact decision boundary between ID and OOD data.
Extensive experiments demonstrate the superiority of DOS, reducing the average
FPR95 by up to 25.79% on CIFAR-100 with TI-300K.

该论文指出样本多样性对于 ODD 检测的关键作用，在此基础上提出一种名为 DOS（多元异常值采样）的采样策略，可以有效地降低平均 FPR95。