Unsupervised Anomaly Detection with Self-Training and Knowledge Distillation
Author:
Affiliation:
1. Tsinghua University,Tsinghua Shenzhen International Graduate School,ShenZhen,China
2. NEC Labs,America
3. ETH Zurich
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9897158/9897159/09897777.pdf?arnumber=9897777
Reference25 articles.
1. OCGAN: One-Class Novelty Detection Using GANs With Constrained Latent Representations
2. f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks
3. PaDiM: A patch distribution modeling framework for anomaly detection and localization;defard;ICPR,2021
4. Sub-image anomaly detection with deep pyramid correspondences;cohen,2020
5. TrustMAE: A Noise-Resilient Defect Classification Framework using Memory-Augmented Auto-Encoders with Trust Regions
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