AnoSeg: Anomaly Segmentation Network Using Self-Supervised Learning

Song, Jouwon; Kong, Kyeongbo; Park, Ye-In; Kim, Seong-Gyun; Kang, Suk-Ju

doi:10.48550/arxiv.2110.03396

Cited by 9 publications

(18 citation statements)

References 16 publications

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“…Besides semantic anomaly detection, self-supervised methods show satisfactory performance for defect detection and spotting sensory anomalies [113]- [115]. Fig.…”

Section: Comparative Evaluation and Discussionmentioning

confidence: 99%

Self-Supervised Anomaly Detection: A Survey and Outlook

Hojjati¹,

Ho²,

Armanfard³

2022

Preprint

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Over the past few years, anomaly detection, a subfield of machine learning that is mainly concerned with the detection of rare events, witnessed an immense improvement following the unprecedented growth of deep learning models. Recently, the emergence of self-supervised learning has sparked the development of new anomaly detection algorithms that surpassed state-of-the-art accuracy by a significant margin. This paper aims to review the current approaches in self-supervised anomaly detection. We present technical details of the common approaches and discuss their strengths and drawbacks. We also compare the performance of these models against each other and other state-of-the-art anomaly detection models. Finally, we discuss a variety of new directions for improving the existing algorithms. 1

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“…Besides semantic anomaly detection, self-supervised methods show satisfactory performance for defect detection and spotting sensory anomalies [113]- [115]. Fig.…”

Section: Comparative Evaluation and Discussionmentioning

confidence: 99%

Self-Supervised Anomaly Detection: A Survey and Outlook

Hojjati¹,

Ho²,

Armanfard³

2022

Preprint

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“…In the unsupervised setting, the training data are all anomaly-free data. Hence, there are some algorithms [104,[106][107][108][109] that adopt the method of creating anomalies. To overcome the limitation of insufficient data, augmentation algorithms [126,127] have been widely used in deep learning scheme.…”

Section: Data Augmentation Based Methodsmentioning

confidence: 99%

“…AnoSeg [109] is a segmentation model which combines three techniques: self-supervised learning with hard augmentation, adversarial learning, and coordinate channel connectivity. It is directly trained for anomaly segmentation task with synthetic anomaly data generated by hard augmentation.…”

Section: Data Augmentation Based Methodsmentioning

confidence: 99%

A Survey on Unsupervised Industrial Anomaly Detection Algorithms

Cui¹,

Liu²,

Lian³

2022

Preprint

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In line with the development of Industry 4.0, more and more attention is attracted to the field of surface defect detection. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in industry field, where deep learning-based algorithms performs better than traditional vision inspection methods in recent years. While existing deep learning-based algorithms are biased towards supervised learning, which not only necessitates a huge amount of labeled data and a significant amount of labor, but it is also inefficient and has certain limitations. In contrast, recent research shows that unsupervised learning has great potential in tackling above disadvantages for visual anomaly detection. In this survey, we summarize current challenges and provide a thorough overview of recently proposed unsupervised algorithms for visual anomaly detection covering five categories, whose innovation points and frameworks are described in detail. Meanwhile, information on publicly available datasets containing surface image samples are provided. By comparing different classes of methods, the advantages and disadvantages of anomaly detection algorithms are summarized. It is expected to assist both the research community and industry in developing a broader and cross-domain perspective.

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“…The first is a reconstruction-based method that uses a generative model to detect abnormalities based on reconstruction errors when input images are rebuilt [6][7][8][9][10][11][12][13][14][15][16].…”

Section: A Anomaly Detecion Using Deep Learningmentioning

confidence: 99%

SA-PatchCore: Anomaly Detection in Dataset With Co-Occurrence Relationships Using Self-Attention

et al. 2023

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Various unsupervised anomaly detection methods using deep learning have recently been proposed, and the accuracy of the anomaly detection technique for local anomalies has been improved. However, no anomaly detection dataset includes co-occurrence-related anomalies, which are combinationrelated. Thus, the accuracy of anomaly detection for co-occurrence-related anomalies has not progressed. Therefore, we propose SA-PatchCore, which introduces self-attention to the state-of-the-art local anomaly detection model, PatchCore. It detects anomalies in co-occurrence relationships and anomalies in local areas with the benefit of the self-attention module, which can consider contexts between separated words introduced first in the natural language processing field. As no anomaly detection dataset includes anomalies in co-occurrence relation, we prepared a new dataset called the Co-occurrence Anomaly Detection Screw Dataset (CAD-SD). Furthermore, we performed experiments on anomaly detection using the new dataset. SA-PatchCore achieves high anomaly detection performance compared with PatchCore in the CAD-SD. Moreover, our proposed model shows almost the same anomaly detection performance as PatchCore in an MVTec Anomaly Detection dataset, which is composed of anomalies in a local area. As a contribution to the anomaly detection task, we have released the CAD-SD to the public. This dataset can be downloaded from the following link: https://github.com/IshidaKengo/Co-occurrence-Anomaly-Detection-Screw-Dataset INDEX TERMS Anomaly detection, deep learning, self-attention

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AnoSeg: Anomaly Segmentation Network Using Self-Supervised Learning

Cited by 9 publications

References 16 publications

Self-Supervised Anomaly Detection: A Survey and Outlook

Self-Supervised Anomaly Detection: A Survey and Outlook

A Survey on Unsupervised Industrial Anomaly Detection Algorithms

SA-PatchCore: Anomaly Detection in Dataset With Co-Occurrence Relationships Using Self-Attention

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