  We propose the novel framework for anomaly detection in images. Our new
framework, PNUNet, is based on many normal data and few anomalous data. We
assume that some noises are added to the input images and learn to remove the
noise. In addition, the proposed method achieves significant performance
improvement by updating the noise assumed in the inputs using a self-training
framework. The experimental results for the benchmark datasets show the
usefulness of our new anomaly detection framework.
