Two-stream Decoder Feature Normality Estimating Network for Industrial Anomaly Detection

Autor:	Park, Chaewon, Lee, Minhyeok, Cho, Suhwan, Kim, Donghyeong, Lee, Sangyoun
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Image reconstruction-based anomaly detection has recently been in the spotlight because of the difficulty of constructing anomaly datasets. These approaches work by learning to model normal features without seeing abnormal samples during training and then discriminating anomalies at test time based on the reconstructive errors. However, these models have limitations in reconstructing the abnormal samples due to their indiscriminate conveyance of features. Moreover, these approaches are not explicitly optimized for distinguishable anomalies. To address these problems, we propose a two-stream decoder network (TSDN), designed to learn both normal and abnormal features. Additionally, we propose a feature normality estimator (FNE) to eliminate abnormal features and prevent high-quality reconstruction of abnormal regions. Evaluation on a standard benchmark demonstrated performance better than state-of-the-art models. Comment: Accepted to IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2302.09794 Zobrazit plný text záznamu View this record from Arxiv