Self-Supervision-Augmented Deep Autoencoder for Unsupervised Visual Anomaly Detection

Chao Huang, Jie Wen, Yong Xu, Qiuping Jiang, Jian Yang, Yaowei Wang

Abstract: Deep autoencoder has demonstrated promising performances in visual anomaly detection. Learning normal patterns on normal data, deep autoencoder is expected to yield larger reconstruction errors for anomalous samples, which is utilized as the criterion for detecting anomalies. However, this hypothesis cannot be always tenable, since the deep autoencoder usually captures the low-level shared features between normal and abnormal data, which leads to similar reconstruction errors for them. To tackle this problem, we propose a self-supervised representation-augmented deep autoencoder for unsupervised visual anomaly detection, which can enlarge the gap of anomaly scores between normal and abnormal samples by introducing autoencoding transformation. Essentially, autoencoding transformation is introduced to facilitate autoencoder to learn the high-level visual semantic features of normal images by introducing a self-supervision task (transformation reconstruction). In particular, our model inputs the original and transformed images into encoder for obtaining latent representations, afterwards they are fed to the decoder for reconstructing both the original image and applied transformation. In this way, our model can utilize both image and transformation reconstruction errors to detect anomaly. Extensive experiments indicate that the proposed method outperforms other state-of-the-art methods, which demonstrates the validity and advancement of our model.