Unsupervised Anomaly Detection Using Style Distillation

Chung, Hwehee; Park, Jong-Ho; Keum, Jongsoo; Ki, Hongdo; Kang, Seokho

doi:10.1109/access.2020.3043473

Cited by 20 publications

(8 citation statements)

References 12 publications

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“…Loss Function Pre-trained Highlights (1) Autoencoder Model [68] L2, SSIM -The paper firstly takes SSIM as a loss to reconstruct image and detect anomalies. [69] L2, SSIM -The paper proposes two AEs and reduces style change during image reconstruction. UTAD [70] L1, Adversarial VGG The paper uses two-stage reconstruction to generate high-fidelity images to avoid reconstruction errors.…”

Section: Methodsmentioning

confidence: 99%

“…There are regularly differences in style between the reconstructed image and the original image, resulting in over-detection. Chung et al [69] present an Outlier-Exposed Style Distillation Network (OE-SDN) to preserve the style translation and suppress the content translation of the AE in order to avoid over-detection. As the anomaly prediction, Chung et al replaced the difference between the original image and the reconstruction image of AE with the difference between the reconstruction image of OE-SDN and the reconstruction image of AE.…”

Section: Autoencodermentioning

confidence: 99%

See 1 more Smart Citation

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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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

“…To improve the quality of reconstruction images, SSIM-AE [7], [8] propose to train an autoencoder with the structural similarity loss for comparing luminance, contrast and structural information between local image regions. MemAE [18] and OE-SDN [19] use an additional method like memory module or knowledge distillation to complement reconstruction images. Two methods are mainly focused on a classification task of anomaly detection such as MNIST and CIFAR-10.…”

Section: Relatied Work a Reconstruction-based Methodsmentioning

confidence: 99%

“…To avoid this drawback, Gong et al [18] used AE with the memory module to correct the reconfiguration error of the abnormal sample. On the other hand, Chung [19] solved the problem by restoring the image using knowledge distillation and outlier exposure regularization. VOLUME 4, 2016 Recently, embedding similarity-based methods in [13], [14], [15], [1] showed good performance using feature vectors of a pre-trained network to detect the abnormality.…”

mentioning

confidence: 99%

Spatial Contrastive Learning for Anomaly Detection and Localization

et al. 2022

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With the development of deep learning, abnormal detection methods have been widely presented to improve performances in various applications, including visual inspection systems. However, there remains difficult to be directly applied to real-world applications, which often include the lack of abnormal samples and diversity. This paper proposes contra embedding that adopts progressive autoencoder with contrastive learning to address these difficulties. The autoencoder is trained progressively to reproduce the details of the original images, and modified CutPaste augmentation helps to learn to recover normal images. Especially, contrastive learning based on normal embedding vectors effectively reduces false positives caused by the autoencoder. The proposed method is also helpful when normal data have complex shapes, sizes, and colors. In experiments, MVTec AD dataset is used to show the generalization ability of the proposed method in various real-world applications. It achieves over 98.0% AUROCs in detection and 97.7% AUROCs in the localization, respectively, without using the ImageNet pre-trained model as in previous methods. INDEX TERMS deep learning, anomaly detection, progressive autoencoder, contrastive learning I. INTRODUCTION Recently, huge performance improvement has been achieved in the unsupervised anomaly detection task, which aims to detect unusual events of test data by only training unlabeled data [1], [2]. Unsupervised anomaly detection is assumed that the training dataset does not usually have abnormal samples, and it can be used when normal samples occur more frequently than abnormal ones. This method can be applied to cases that are difficult to collect abnormal data, such as medical and industrial applications [3], [4], [5], [6].

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“…Anomaly Detection. A wide range of literature on anomaly detection [18]- [26] has appeared in machine learning. In most anomaly detection tasks, we assume that only the normal data is given during the training and the model predicts whether a test sample is normal or not during the test time.…”

Section: Related Workmentioning

confidence: 99%

Self-Supervised Learning for Anomaly Detection With Dynamic Local Augmentation

et al. 2021

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Anomaly detection is an important problem for recent advances in machine learning. To this end, many attempts have emerged to detect unknown anomalies of the images by learning representations and designing score functions. In this paper, we propose a simple yet effective framework for unsupervised anomaly detection using self-supervised learning. We extend conventional self-supervised learning for an anomaly detection problem. In anomaly detection, anomalous patterns appear in the local regions of an image, so we employ dynamic local augmentation to generate a negative pair of the images from the normal training dataset. Specifically, in addition to learning the global representation of an image, our framework contrasts a normal sample to a locally augmented sample. To effectively apply the local augmentations regardless of a category or a random location of an image, we use dynamically weighted local augmentations to generate more suitable negative samples. We also present a novel scoring function for detecting unseen anomalous patterns. Our experiment demonstrates the effectiveness of our method, and we show that our framework achieves competitive performance compared to state-of-the-art methods on MVTec Anomaly Detection dataset.

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Unsupervised Anomaly Detection Using Style Distillation

Cited by 20 publications

References 12 publications

Deep Industrial Image Anomaly Detection: A Survey

Deep Industrial Image Anomaly Detection: A Survey

Spatial Contrastive Learning for Anomaly Detection and Localization

Self-Supervised Learning for Anomaly Detection With Dynamic Local Augmentation

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