Augment to Detect Anomalies with Continuous Labelling

TL;DR

This paper introduces a lightweight, supervised regression approach for anomaly detection that uses continuous labeling and data augmentation to distinguish normal data from anomalies, achieving state-of-the-art results.

Contribution

It proposes a novel anomaly detection method that treats the problem as a supervised regression task using continuous labels and augmentation, simplifying training and improving performance.

Findings

Outperforms state-of-the-art methods on image datasets

Uses simple augmentation to simulate anomalies effectively

Achieves high accuracy with lightweight CNNs

Abstract

Anomaly detection is to recognize samples that differ in some respect from the training observations. These samples which do not conform to the distribution of normal data are called outliers or anomalies. In real-world anomaly detection problems, the outliers are absent, not well defined, or have a very limited number of instances. Recent state-of-the-art deep learning-based anomaly detection methods suffer from high computational cost, complexity, unstable training procedures, and non-trivial implementation, making them difficult to deploy in real-world applications. To combat this problem, we leverage a simple learning procedure that trains a lightweight convolutional neural network, reaching state-of-the-art performance in anomaly detection. In this paper, we propose to solve anomaly detection as a supervised regression problem. We label normal and anomalous data using two separable distributions of continuous values. To compensate for the unavailability of anomalous samples during training time, we utilize straightforward image augmentation techniques to create a distinct set of samples as anomalies. The distribution of the augmented set is similar but slightly deviated from the normal data, whereas real anomalies are expected to have an even further distribution. Therefore, training a regressor on these augmented samples will result in more separable distributions of labels for normal and real anomalous data points. Anomaly detection experiments on image and video datasets show the superiority of the proposed method over the state-of-the-art approaches.