Due to the intractability of characterizing everything that looks unlike the
normal data, anomaly detection (AD) is traditionally treated as an unsupervised
problem utilizing only normal samples. However, it has recently been found that
unsupervised image AD can be drastically improved through the utilization of
huge corpora of random images to represent anomalousness; a technique which is
known as Outlier Exposure. In this paper we show that specialized AD learning
methods seem unnecessary for state-of-the-art performance, and furthermore one
can achieve strong performance with just a small collection of Outlier Exposure
data, contradicting common assumptions in the field of AD. We find that
standard classifiers and semi-supervised one-class methods trained to discern
between normal samples and relatively few random natural images are able to
outperform the current state of the art on an established AD benchmark with
ImageNet. Further experiments reveal that even one well-chosen outlier sample
is sufficient to achieve decent performance on this benchmark (79.3% AUC). We
investigate this phenomenon and find that one-class methods are more robust to
the choice of training outliers, indicating that there are scenarios where
these are still more useful than standard classifiers. Additionally, we include
experiments that delineate the scenarios where our results hold. Lastly, no
training samples are necessary when one uses the representations learned by
CLIP, a recent foundation model, which achieves state-of-the-art AD results on
CIFAR-10 and ImageNet in a zero-shot setting.

本论文利用大量的随机图像来代表异常性，通过一些经过改进的学习方法、标准分类器和一类半监督的方法进行训练，发现无论何时在已有数据的基础上添加一个训练异常样本能够达到很不错的结果（在 ImageNet 基准测试下的 AUC 值可以达到 79.3%），并表明一类方法对于训练异常样本的选择更加鲁棒，与标准分类器相比具有优势，并且在使用 CLIP 表示学习时，零样本的情况下可以实现 ImageNet 上的最新异常检测结果。