Anomaly Detection by Context Contrasting
Keywords: anomaly detection, one class classification, novelty detection, contrastive representation learning, context clustering
TL;DR: We propose a novel model for self-supervised representation learning and demonstrate how to perform anomaly detection on the resulting representations.
Abstract: Anomaly detection focuses on identifying samples that deviate from the norm.
When working with high-dimensional data such as images, a crucial requirement for detecting anomalous patterns is learning lower-dimensional representations that capture concepts of normality.
Recent advances in self-supervised learning have shown great promise in this regard.
However, many successful self-supervised anomaly detection methods assume prior knowledge about anomalies to create synthetic outliers during training.
Yet, in real-world applications, we often do not know what to expect from unseen data, and we can solely leverage knowledge about normal data.
In this work, we propose Con$_2$, which learns representations through context augmentations that allow us to observe samples from
two distinct perspectives while keeping the invariances of normal data.
Con$_2$ learns rich representations of context-augmented samples by clustering them according to their context while simultaneously
aligning their positions across clusters.
At test time, representations of anomalies that do not adhere to the invariances of normal data then deviate from their respective context cluster.
Learning representations in such a way thus allows us to detect anomalies without making
assumptions about anomalous data.
Supplementary Material: zip
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 9503
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