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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
In the citing paper
“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
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
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
Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016) · Journal of Big Data · doi:10.1186/s40537-016-0043-6 · Cited by 6,260
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Xiao, H., Rasul, K., & Vollgraf, R. (2017) · arXiv · doi:10.48550/arxiv.1708.07747 · Cited by 6,108
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370
Striving for Simplicity: The All Convolutional Net
Springenberg, J. T., Dosovitskiy, A., Brox, T., & Riedmiller, M. (2014) · arXiv · doi:10.48550/arxiv.1412.6806 · Cited by 3,447
A Survey on Deep Transfer Learning
Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01424-7_27 · Cited by 3,165
FitNets: Hints for Thin Deep Nets
Romero, A., Ballas, N., Kahou, S. E., Chassang, A., Gatta, C., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.6550 · Cited by 2,859
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., & Langs, G. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-59050-9_12 · Cited by 2,627
MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection
Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00982 · Cited by 2,203
Ruff, L., Vandermeulen, R. A., Görnitz, N., Deecke, L., Siddiqui, S. A., Binder, A., Müller, E., & Kloft, M. (2018) · International Conference on Machine Learning · Cited by 1,719
A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning
Yim, J., Joo, D., Bae, J., & Kim, J. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.754 · Cited by 1,668
f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks
Schlegl, T., Seeböck, P., Waldstein, S. M., Langs, G., & Schmidt-Erfurth, U. M. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.010 · Cited by 1,645
Anomaly Detection with Robust Deep Autoencoders
Zhou, C., & Paffenroth, R. C. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098052 · Cited by 1,607
SmoothGrad: removing noise by adding noise
Smilkov, D., Thorat, N., Kim, B., Viégas, F., & Wattenberg, M. (2017) · arXiv · doi:10.48550/arxiv.1706.03825 · Cited by 1,467
Sanity Checks for Saliency Maps
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., & Kim, B. (2018) · arXiv · doi:10.48550/arxiv.1810.03292 · Cited by 1,294
Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings
Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00424 · Cited by 990
Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders
Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., & Steger, C. T. (2019) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0007364503720380 · Cited by 893
Adversarially Learned One-Class Classifier for Novelty Detection
Sabokrou, M., Khalooei, M., Fathy, M., & Adeli, E. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00356 · Cited by 765
GANomaly: Semi-supervised Anomaly Detection via Adversarial Training
Akçay, S., Atapour–Abarghouei, A., & Breckon, T. P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20893-6_39 · Cited by 706
One-class SVM for learning in image retrieval
Chen, Y., Zhou, X. S., & Huang, T. S. (2002) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2001.958946 · Cited by 646
OCGAN: One-Class Novelty Detection Using GANs With Constrained Latent Representations
Perera, P., Nallapati, R., & Xiang, B. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00301 · Cited by 608
Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images
Baur, C., Wiestler, B., Albarqouni, S., & Navab, N. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11723-8_16 · Cited by 516
Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes
Sabokrou, M., Fayyaz, M., Fathy, M., Moayed, Z., & Klette, R. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.02.006 · Cited by 514
Latent Space Autoregression for Novelty Detection
Abati, D., Porrello, A., Calderara, S., & Cucchiara, R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00057 · Cited by 508
Deep Anomaly Detection Using Geometric TransformationsPreprint
Golan, I., & El‐Yaniv, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10917 · Cited by 479
Deep Semi-Supervised Anomaly Detection
Ruff, L., Vandermeulen, R. A., Görnitz, N., Binder, A., Müller, E., Müller, K.-R., & Kloft, M. (2019) · arXiv · doi:10.48550/arxiv.1906.02694 · Cited by 388
Thoracic Disease Identification and Localization with Limited Supervision
Li, Z., Wang, C., Han, M., Xue, Y., Wei, W., Li, L.-J., & Fei-Fei, L. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00865 · Cited by 346
Deep Structured Energy Based Models for Anomaly Detection
Zhai, S., Cheng, Y., Lu, W., & Zhang, Z. (2016) · arXiv · doi:10.48550/arxiv.1605.07717 · Cited by 334
Classification-Based Anomaly Detection for General DataPreprint
Bergman, L., & Hoshen, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.02359 · Cited by 294
Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity
Napoletano, P., Piccoli, F., & Schettini, R. (2018) · Sensors · doi:10.3390/s18010209 · Cited by 285
Mei, S., Wang, Y., & Wen, G. (2018) · Sensors · doi:10.3390/s18041064 · Cited by 284
Attention Guided Anomaly Localization in Images
Venkataramanan, S., Peng, K.-C., Singh, R. V., & Mahalanobis, A. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58520-4_29 · Cited by 247
Loss Functions for Neural Networks for Image ProcessingPreprint
Zhao, H., Gallo, O., Frosio, I., & Kautz, J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.08861 · Cited by 236
OCTID: Optical coherence tomography image database
Gholami, P., Roy, P., Parthasarathy, M. K., & Lakshminarayanan, V. (2019) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2019.106532 · Cited by 195
Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images
Zhou, K., Xiao, Y., Yang, J., Cheng, J., Liu, W., Luo, W., Gu, Z., Liu, J., & Gao, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58565-5_22 · Cited by 146
A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based VisualizationsPreprint
Nie, W., Zhang, Y., & Patel, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.07039 · Cited by 115
Iterative energy-based projection on a normal data manifold for anomaly localizationPreprint
Dehaene, D., Frigo, O., Combrexelle, S., & Eline, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.03734 · Cited by 112
DROCC: Deep Robust One-Class ClassificationPreprint
Goyal, S., Raghunathan, A., Jain, M., Simhadri, H. V., & Jain, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.12718 · Cited by 112
Hazard, J. N. (1960) · Columbia University Press eBooks · doi:10.7312/haza92762-003 · Cited by 110
AVID: Adversarial Visual Irregularity Detection
Sabokrou, M., Pourreza, M., Fayyaz, M., Entezari, R., Fathy, M., Gall, J., & Adeli, E. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20876-9_31 · Cited by 106
Transfer representation-learning for anomaly detection
Andrews, J. T. A., Tanay, T., Morton, E., & Griffin, L. D. (2016) · UCL Discovery (University College London) · Cited by 94
ARAE: Adversarially robust training of autoencoders improves novelty detection
Salehi, M., Arya, A., Pajoum, B., Otoofi, M., Shaeiri, A., Rohban, M. H., & Rabiee, H. R. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.09.014 · Cited by 61
Self-citationPuzzle-AE: Novelty Detection in Images through Solving PuzzlesPreprint
Salehi, M., Eftekhar, A., Sadjadi, N., Rohban, M. H., & Rabiee, H. R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.12959 · Cited by 61
Self-citationWhere's Wally Now? Deep Generative and Discriminative Embeddings for Novelty Detection
Burlina, P., Joshi, N., & Wang, I.-J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01177 · Cited by 51
Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?Preprint
de Nazaré, T. S., de Mello, R. F., & Ponti, M. A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.08495 · Cited by 44
A Case for the Score: Identifying Image Anomalies using Variational Autoencoder GradientsPreprint
Zimmerer, D., Petersen, J., Köhl, S., & Maier-Hein, K. H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.00003 · Cited by 21
Exploring Deep Anomaly Detection Methods Based on Capsule Net
Li, X., Kiringa, I., Yeap, T. H., Zhu, X., & Li, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-47358-7_39 · Cited by 19
Kitamura, F. C. (2023) · Kaggle · doi:10.34740/kaggle/dsv/152137 · Cited by 9
Region-Based Anomaly Localisation in Crowded Scenes via Trajectory Analysis and Path Prediction
Zhang, T., Wiliem, A., & Lovell, B. C. (2013) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2013.6691519 · Cited by 6
Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders
Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., & Steger, C. (2019) · Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications · doi:10.5220/0007364500002108
Flagged first, then by citations
2 of the 57 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- 10.1109/CVPR42600.2020.00424 · DOI · Paper page
- 10.5220/0007364500002108 · DOI
Cited by
Sorted by citations
Towards Total Recall in Industrial Anomaly Detection
Roth, K., Pemula, L., Zepeda, J., Scholkopf, B., Brox, T., & Gehler, P. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01392 · Cited by 1,810
Deep Industrial Image Anomaly Detection: A Survey
Liu, J., Xie, G., Wang, J., Li, S., Wang, C., Zheng, F., & Jin, Y. (2024) · Machine Intelligence Research · doi:10.1007/s11633-023-1459-z · Cited by 463
“While STPM [11] and MKD [10] both use multi-scale features under different network layers for distillation, they do so in different ways.”
A Unified Model for Multi-Class Anomaly Detection
You, Z., Cui, L., Shen, Y., Yang, K., Lu, X., Zheng, Y., & Le, X. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-0330 · Cited by 303
Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey
Tao, X., Gong, X., Zhang, X., Yan, S., & Adak, C. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3196436 · Cited by 228
A Diffusion-Based Framework for Multi-Class Anomaly Detection
He, H., Zhang, J., Chen, H., Chen, X., Li, Z., Chen, X., Wang, Y., Wang, C., & Xie, L. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i8.28690 · Cited by 271
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
