Unsupervised Anomaly Segmentation Via Multilevel Image Reconstruction and Adaptive Attention-Level Transition

Yan, Yi; Wang, Deming; Zhou, Guangliang; Chen, Qijun

doi:10.1109/tim.2021.3107586

Cited by 27 publications

(7 citation statements)

References 32 publications

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“…DFR [71] L2 VGG The paper proposes to reconstruct and compare at the feature level to detect anomalies. ALT [72] L1, Perceptual, Adversarial…”

Section: Methodsmentioning

confidence: 99%

“…Then, DFR reconstructs features using a deep yet efficient convolutional AE and detects anomalous regions by comparing the original features to the reconstruction features. Yan et al [72] propose a novel Multi-Level Image Reconstruction (MLIR) framework that forms the reconstruction process as an image denoising task at different resolutions. Thus, MLIR accounts for the detection of both global structure anomalies and detail anomalies.…”

Section: Autoencodermentioning

confidence: 99%

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Deep Industrial Image Anomaly Detection: A Survey

Liu¹,

Xie²,

Wang³

et al. 2023

Preprint

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The recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the new setting from industrial manufacturing and review the current IAD approaches under our proposed our new setting. Moreover, we highlight several opening challenges for image anomaly detection. The merits and downsides of representative network architectures under varying supervision are discussed. Finally, we summarize the research findings and point out future research directions. More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection.

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“…DFR [71] L2 VGG The paper proposes to reconstruct and compare at the feature level to detect anomalies. ALT [72] L1, Perceptual, Adversarial…”

Section: Methodsmentioning

confidence: 99%

Section: Autoencodermentioning

confidence: 99%

Deep Industrial Image Anomaly Detection: A Survey

Liu¹,

Xie²,

Wang³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…DFR [78] L2 VGG The paper proposes to reconstruct and compare at the feature level to detect anomalies.…”

Section: L2mentioning

confidence: 99%

“…Reconstruction-based methods are nearly effective as feature embedding methods when utilizing features at different scales. Similar to teacher-student architecture, deep feature reconstruction (DFR) [78] method detects anomalous through reconstruction at the level of features. DFR obtains multiple spatial context-aware representations from a network that has been pre-trained.…”

Section: L2mentioning

confidence: 99%

“…One reason for this is the domain differences between medical and industrial image datasets, and another reason is the lack of a good baseline and benchmark for comparison. It would be very meaningful to establish a unified framework for both industrial and medical image an- CFA [69] 1.000 [76] 0.940 0.840 0.920 0.950 0.970 0.970 0.850 0.890 0.940 0.970 0.810 0.980 0.880 0.790 0.870 0.920 DFR [78] 0.970 0.920 EdgRec [84] 0 [98] 0.922 0.910 De Nardin et al [112] 0…”

Section: Future Directionsmentioning

confidence: 99%

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Deep Industrial Image Anomaly Detection: A Survey

Liu,

Xie,

Wang

et al. 2024

Mach. Intell. Res.

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The recent rapid development of deep learning has laid a milestone in industrial image anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the promising setting from industrial manufacturing and review the current IAD approaches under our proposed setting. Moreover, we highlight several opening challenges for image anomaly detection. The merits and downsides of representative network architectures under varying supervision are discussed. Finally, we summarize the research findings and point out future research directions. More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection.

show abstract