The DOI matches its Crossref record; 55 of the 57 references it cites were found in a registry or an index.
Notice of Removal: Multiresolution Knowledge Distillation for Anomaly Detection
Open accessAbstract
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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“The exact computation of M ∗ C is NP-Hard [54], we use the iterative greedy approximation suggested in [48].”
From Towards Total Recall in Industrial Anomaly Detection (2022) · cites this paper as [54]
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“In addition, MKD finds that the lighter student network structure performs better than the student network structure identical to that of the teacher network.”
From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [11]
Citations
Cites
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Unpaired image-to-image translation using cycle-consistent adversarial networks
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Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00068 · Cited by 14,623
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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
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2 of the 57 references couldn’t be matched to a known paper.
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- 10.1109/CVPR42600.2020.00424 · DOI · Paper page
- 10.5220/0007364500002108 · DOI
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Minding Anomaly Patterns via Uncertainty-Guided Heterogeneous Knowledge Distillation with Denoising
Li, Y., Yang, S., Wang, Y., Wang, B., Qi, J., & Shen, Q. (2026) · Big Data Mining and Analytics · doi:10.26599/bdma.2026.9020006
Metadata
- Authors
- Salehi, M., Sadjadi, N., Baselizadeh, S., Rohban, M. H., & Rabiee, H. R.
- Affiliations
- Sharif University of Technology (IR)
- 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)
- 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
- 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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