E$^{3}$3Outlier: a Self-Supervised Framework for Unsupervised Deep Outlier Detection

Existing unsupervised outlier detection (OD) solutions face a grave challenge with surging visual data like images. Although deep neural networks (DNNs) prove successful for visual data, deep OD remains difficult due to OD's unsupervised nature. This paper proposes a novel framework named
E$^{3}$3Outlier
that can perform
e
ffective and
e
nd-to-
e
nd deep outlier removal. Its core idea is to introduce
self-supervision
into deep OD. Specifically, our major solution is to adopt a discriminative learning paradigm that creates multiple pseudo classes from given unlabeled data by various data operations, which enables us to apply prevalent discriminative DNNs (e.g., ResNet) to the unsupervised OD problem. Then, with theoretical and empirical demonstration, we argue that inlier priority, a property that encourages DNN to prioritize inliers during self-supervised learning, makes it possible to perform end-to-end OD. Meanwhile, unlike frequently-used outlierness measures (e.g., density, proximity) in previous OD methods, we explore network uncertainty and validate it as a highly effective outlierness measure, while two practical score refinement strategies are also designed to improve OD performance. Finally, in addition to the discriminative learning paradigm above, we also explore the solutions that exploit other learning paradigms (i.e., generative learning and contrastive learning) to introduce self-supervision for
E$^{3}$3Outlier
. Such extendibility not only brings further performance gain on relatively difficult datasets, but also enables
E$^{3}$3Outlier
to be applied to other OD applications like video abnormal event detection. Extensive experiments demonstrate that
E$^{3}$3Outlier
can considerably outperform state-of-the-art counterparts by 10%-30% AUROC. Demo codes are available at
https://github.com/demonzyj56/E3Outlier
.