Self-Supervised Learning for Anomaly Detection With Dynamic Local Augmentation

Abstract

Anomaly detection is an important problem for recent advances in machine learning. To this end, many attempts have emerged to detect unknown anomalies of the images by learning representations and designing score functions. In this paper, we propose a simple yet effective framework for unsupervised anomaly detection using self-supervised learning. We extend conventional self-supervised learning for an anomaly detection problem. In anomaly detection, anomalous patterns appear in the local regions of an image, so we employ dynamic local augmentation to generate a negative pair of the images from the normal training dataset. Specifically, in addition to learning the global representation of an image, our framework contrasts a normal sample to a locally augmented sample. To effectively apply the local augmentations regardless of a category or a random location of an image, we use dynamically weighted local augmentations to generate more suitable negative samples. We also present a novel scoring function for detecting unseen anomalous patterns. Our experiment demonstrates the effectiveness of our method, and we show that our framework achieves competitive performance compared to state-of-the-art methods on MVTec Anomaly Detection dataset.