Semi-supervised anomaly detection with dual prototypes autoencoder for industrial surface inspection

GEV-NN: A deep neural network architecture for class imbalance problem in binary classification

(2020) Lkhagvadorj Munkhdalai et al.   KNOWLEDGE-BASED SYSTEMS

Surface defect classification of steels with a new semi-supervised learning method

(2019) He Di et al.   OPTICS AND LASERS IN ENGINEERING

AnomalyNet: An Anomaly Detection Network for Video Surveillance

(2019) Joey Tianyi Zhou et al.   IEEE Transactions on Information Forensics and Security

Semi-supervised defect classification of steel surface based on multi-training and generative adversarial network

(2019) Yu He et al.   OPTICS AND LASERS IN ENGINEERING

A deep-learning-based approach for fast and robust steel surface defects classification

(2019) Guizhong Fu et al.   OPTICS AND LASERS IN ENGINEERING

Unified detection method of aluminium profile surface defects: Common and rare defect categories

(2019) Defu Zhang et al.   OPTICS AND LASERS IN ENGINEERING

An End-to-End Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features

(2019) Yu He et al.   IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

A survey on deep learning in medical image analysis

(2017) Geert Litjens et al.   MEDICAL IMAGE ANALYSIS

Application of Shearlet transform to classification of surface defects for metals

(2015) Ke Xu et al.   IMAGE AND VISION COMPUTING

A survey on unsupervised outlier detection in high-dimensional numerical data

(2012) Arthur Zimek et al.   Statistical Analysis and Data Mining

Anomaly detection

(2009) Varun Chandola et al.   ACM COMPUTING SURVEYS

Fabric defect detection using morphological filters

(2009) K.L. Mak et al.   IMAGE AND VISION COMPUTING