Cited by 101Anomaly Detection Techniques and ApplicationsAdversarial Robustness in Machine LearningIndustrial Vision Systems and Defect Detection

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Notice of Removal: Multiresolution Knowledge Distillation for Anomaly Detection

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Salehi, Mohammadreza, Sadjadi, Niousha, Baselizadeh, Soroosh, Rohban, Mohammad H., Rabiee, Hamid R.·2021·2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

DOI 10.1109/cvpr46437.2021.01466

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Abstract

Unsupervised representation learning has proved to be a critical component of anomaly detection/localization in images. The challenges to learn such a representation are two-fold. Firstly, the sample size is not often large enough to learn a rich generalizable representation through conventional techniques. Secondly, while only normal samples are available at training, the learned features should be discriminative of normal and anomalous samples. Here, we propose to use the "distillation" of features at various layers of an expert network, which is pre-trained on ImageNet, into a simpler cloner network to tackle both issues. We detect and localize anomalies using the discrepancy between the expert and cloner networks’ intermediate activation values given an input sample. We show that considering multiple intermediate hints in distillation leads to better exploitation of the expert’s knowledge and a more distinctive discrepancy between the two networks, compared to utilizing only the last layer activation values. Notably, previous methods either fail in precise anomaly localization or need expensive region-based training. In contrast, with no need for any special or intensive training procedure, we incorporate interpretability algorithms in our novel framework to localize anomalous regions. Despite the striking difference between some test datasets and ImageNet, we achieve competitive or significantly superior results compared to SOTA on MNIST, F-MNIST, CIFAR-10, MVTecAD, Retinal-OCT, and two other medical datasets on both anomaly detection and localization.

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Authors: Salehi, M., Sadjadi, N., Baselizadeh, S., Rohban, M. H., & Rabiee, H. R.
Affiliations: Sharif University of Technology (IR)
ORCID: Salehi, Mohammadreza
Rohban, Mohammad H.
Rabiee, Hamid R.
Venue: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher: IEEE
Published: 2021-06
Pages: 14897-14907
ISSN: 1063-6919 (ISSN-L)
DOI: 10.1109/cvpr46437.2021.01466
OpenAlex: W3108261849
Cited by: 97
References: 57
Citations by year: 2026: 22 · 2025: 19 · 2024: 22 · 2023: 20 · 2022: 11 · 2021: 3
FWCI: 6.47
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
arXiv (Cornell University) (submittedVersion)
UvA-DARE (University of Amsterdam) (submittedVersion, other-oa)
Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
Keywords: anomaly detection, anomaly localization, knowledge distillation, unsupervised feature learning, expert networks, interpretability, medical image anomaly detection, retinal OCT

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Notice of Removal: Multiresolution Knowledge Distillation for Anomaly Detection

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