PNUNet: Anomaly Detection using Positive-and-Negative Noise based on Self-Training Procedure
Year: 2019
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Author: Kimura, Masanari
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2945590966