CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows

Gudovskiy, Denis; Ishizaka, Shun; Kozuka, Kazuki

doi:10.48550/arxiv.2107.12571

Cited by 6 publications

(12 citation statements)

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“…For MVTec AD dataset for 2D task, we report the performance comparison of Glance [31], DRAEM [35], DFR [32], R-D [9], PaDim [8], P-SVDD [33], FYD [37], SPADE [7], PANDA [20], CutPaste [18], NSA [28], CFlow [14], FastFlow [34], PatchCore [22] in terms of the image-level and pixel-level metrics. The inference efficiencies of some of these methods are also provided.…”

Section: Resultsmentioning

confidence: 99%

“…The inference efficiencies of some of these methods are also provided. For MVTec 3D-AD dataset for 3D task, we give the experiment results of DifferNet [23], PaDim [8], PatchCore, CFlow [14], CSFlow [24] and FastFlow [34].…”

Section: Resultsmentioning

confidence: 99%

“…Representation-based methods adopt a different methodology in Fig. 3, which first uses different feature extractors to obtain discriminative features for normal images [3,21,23,25] or normal image patches [7,14,20,33], and then establish the distribution of these normal features. Finally, the anomaly score is calculated by the distance between the embedding of a test image and the distribution of normal image representations.…”

Section: Representation-based Methodsmentioning

confidence: 99%

“…Patch-Core [22] uses a maximally representative memory bank of normal patch-features and uses the k-nearest neighbor method to search for the closest feature for distance metric. Recently, some works [14,23,34] began to use parametric methods such as normalizing flow [10,16] to estimate the distribution of normal images. Through a trainable process that maximizes the log-likelihood of normal image features, they embed normal image features into standard normal distribution and use the probability to identify and locate anomalies.…”

Section: Representation-based Methodsmentioning

confidence: 99%

“…Unfortunately, this work failed to obtain the exact anomaly localization results since global average pooling was used. CFLOW-AD [14] proposes to use hard code position embedding to leverage the distribution learned by NF, which probably underperforms at more complicated datasets. CSFlow [24] proposes a novel fully convolutional cross-scale normalizing flow to jointly processes multiple feature maps of different scales.…”

Section: Representation-based Methodsmentioning

confidence: 99%

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Benchmarking Unsupervised Anomaly Detection and Localization

Zheng¹,

Wang²,

Qian³

et al. 2022

Preprint

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Unsupervised anomaly detection and localization, as of one the most practical and challenging problems in computer vision, has received great attention in recent years. From the time the MVTec AD dataset was proposed to the present, new research methods that are constantly being proposed push its precision to saturation. It is the time to conduct a comprehensive comparison of existing methods to inspire further research. This paper extensively compares 13 papers in terms of the performance in unsupervised anomaly detection and localization tasks, and adds a comparison of inference efficiency previously ignored by the community. Meanwhile, analysis of the MVTec AD dataset are also given, especially the label ambiguity that affects the model fails to achieve full marks. Moreover, considering the proposal of the new MVTec 3D-AD dataset, this paper also conducts experiments using the existing state-of-the-art 2D methods on this new dataset, and reports the corresponding results with analysis.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Representation-based Methodsmentioning

confidence: 99%

Section: Representation-based Methodsmentioning

confidence: 99%

Section: Representation-based Methodsmentioning

confidence: 99%

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Benchmarking Unsupervised Anomaly Detection and Localization

Zheng¹,

Wang²,

Qian³

et al. 2022

Preprint

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Unsupervised exceptional human action detection from repetition of human assembling tasks using entropy signal clustering

Yang,

Hsu,

Kang

et al. 2024

J Intell Manuf

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CFA: Coupled-Hypersphere-Based Feature Adaptation for Target-Oriented Anomaly Localization

2022

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For a long time, anomaly localization has been widely used in industries. Previous studies focused on approximating the distribution of normal features without adaptation to a target dataset. However, since anomaly localization should precisely discriminate normal and abnormal features, the absence of adaptation may make the normality of abnormal features overestimated. Thus, we propose Coupled-hypersphere-based Feature Adaptation (CFA) which accomplishes sophisticated anomaly localization using features adapted to the target dataset. CFA consists of (1) a learnable patch descriptor that learns and embeds target-oriented features and (2) scalable memory bank independent of the size of the target dataset. And, CFA adopts transfer learning to increase the normal feature density so that abnormal features can be clearly distinguished by applying patch descriptor and memory bank to a pretrained CNN. The proposed method outperforms the previous methods quantitatively and qualitatively. For example, it provides an AUROC score of 99.5% in anomaly detection and 98.5% in anomaly localization of MVTec AD benchmark. In addition, this paper points out the negative effects of biased features of pre-trained CNNs and emphasizes the importance of the adaptation to the target dataset. The code is publicly available at https://github.com/ sungwool/CFA_for_anomaly_localization

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CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows

Cited by 6 publications

References 0 publications

Benchmarking Unsupervised Anomaly Detection and Localization

Benchmarking Unsupervised Anomaly Detection and Localization

Unsupervised exceptional human action detection from repetition of human assembling tasks using entropy signal clustering

CFA: Coupled-Hypersphere-Based Feature Adaptation for Target-Oriented Anomaly Localization

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