Anomaly Detection for Images Using Auto-encoder Based Sparse Representation

Anomaly detection is a pattern recognition task that aims at distinguishing abnormal patterns from normal ones. In this paper, we propose a convolutional auto-encoder based model to detect anomaly images by producing a sparse representation in the latent space. The proposed approach is able to represent the normal images using sparse encoding and the encoding can be well reconstructed by the decoder. However, the learned convolutional filters are not able to represent the abnormal images in a sparse way. Therefore, the decoder can not reconstruct the abnormal images with high quality. By assessing the reconstruction performance, we can distinguish the abnormal images from the normal ones. The experimental results show the superiority of our proposed model over other variants of auto-encoder based anomaly detection models in terms of AUC. In addition, the results show that the sparse representation based anomaly detection method could apply to different scenarios.