The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Revisiting Reverse Distillation for Anomaly Detection
Abstract
Anomaly detection is an important application in large-scale industrial manufacturing. Recent methods for this task have demonstrated excellent accuracy but come with a latency trade-off. Memory based approaches with dominant performances like PatchCore or Coupled-hypersphere-based Feature Adaptation (CFA) require an external memory bank, which significantly lengthens the execution time. Another approach that employs Reversed Distillation (RD) can perform well while maintaining low latency. In this paper, we revisit this idea to improve its performance, establishing a new state-of-the-art benchmark on the challenging MVTec dataset for both anomaly detection and localization. The proposed method, called RD++, runs six times faster than PatchCore, and two times faster than CFA but introduces a negligible latency compared to RD. We also experiment on the BTAD and Retinal OCT datasets to demonstrate our method's generalizability and conduct important ablation experiments to provide insights into its configurations. Source code will be available at https://github.com/tientrandinh/Revisiting-Reverse-Distillation.
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Cites
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Distilling the Knowledge in a Neural Network
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Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
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Instance Normalization: The Missing Ingredient for Fast Stylization
Ulyanov, D., Vedaldi, A., & Lempitsky, V. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.08022 · Cited by 2,775
Empirical Evaluation of Rectified Activations in Convolutional Network
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Metadata
- Authors
- Tien, T. D., Nguyen, A. T., Tran, N. H., Huy, T. D., Duong, S. T. M., Nguyen, C. D. T., & Truong, S. Q. H.
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 24511-24520
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386075837
- Cited by
- 268
- References
- 36
- FWCI
- 50.66
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Tien, T. D., Nguyen, A. T., Tran, N. H., Huy, T. D., Duong, S. T. M., Nguyen, C. D. T., & Truong, S. Q. H. (2023). Revisiting Reverse Distillation for Anomaly Detection. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr52729.2023.02348
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