Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representations

Rippel, Oliver; Chavan, Arnav; Lei, Chucai; Merhof, Dorit

doi:10.48550/arxiv.2108.04116

Cited by 3 publications

(3 citation statements)

References 35 publications

(80 reference statements)

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“…The paper establishes a model of normality by fitting a multivariate Gaussian to feature representations of a pre-trained network. [39] Mahalanobis Distance EfficientNet The paper generates a multi-variate Gaussian distribution for the normal class and mitigates the catastrophic forgetting in past research. PEDENet [40] Log-Likelihood, Cross-Entropy, Regularization -The model can predict the location of the patch and compare it with the actual location to judge the abnormality.…”

Section: Distribution Mapmentioning

confidence: 99%

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: Distribution Mapmentioning

confidence: 99%

Deep Industrial Image Anomaly Detection: A Survey

Liu¹,

Xie²,

Wang³

et al. 2023

Preprint

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“…A different class of methods leverage descriptors from pretrained networks for anomaly detection (Bergmann et al, 2020;Cohen and Hoshen, 2020;Defard et al, 2021;Gudovskiy et al, 2022;Mishra et al, 2020;Reiss et al, 2021;Rippel et al, 2021). The key idea behind these approaches is that anomalous regions produce descriptors that differ from the ones without anomalies.…”

Section: Anomaly Detection In 2dmentioning

confidence: 99%

Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors

Bergmann¹,

Sattlegger²

2022

Preprint

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We present a new method for the unsupervised detection of geometric anomalies in highresolution 3D point clouds. In particular, we propose an adaptation of the established student-teacher anomaly detection framework to three dimensions. A student network is trained to match the output of a pretrained teacher network on anomaly-free point clouds. When applied to test data, regression errors between the teacher and the student allow reliable localization of anomalous structures. To construct an expressive teacher network that extracts dense local geometric descriptors, we introduce a novel self-supervised pretraining strategy. The teacher is trained by reconstructing local receptive fields and does not require annotations. Extensive experiments on the comprehensive MVTec 3D Anomaly Detection dataset highlight the effectiveness of our approach, which outperforms the next-best method by a large margin. Ablation studies show that our approach meets the requirements of practical applications regarding performance, runtime, and memory consumption.

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“…We show that k-means clustering is less successful than our method on this task. Similar techniques were adopted by [30] and improved by [31,34]. Adaptation of pre-trained representations using contrastive learning was recently suggested by [32].…”

Section: Introductionmentioning

confidence: 99%

Out-of-Distribution Detection without Class Labels

Cohen¹,

Abutbul²,

Hoshen³

2021

Preprint

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Anomaly detection methods identify samples that deviate from the normal behavior of the dataset. It is typically tackled either for training sets containing normal data from multiple labeled classes or a single unlabeled class. Current methods struggle when faced with training data consisting of multiple classes but no labels. In this work, we first discover that classifiers learned by self-supervised image clustering methods provide a strong baseline for anomaly detection on unlabeled multi-class datasets. Perhaps surprisingly, we find that initializing clustering methods with pre-trained features does not improve over their self-supervised counterparts. This is due to the phenomenon of catastrophic forgetting. Instead, we suggest a two stage approach. We first cluster images using selfsupervised methods and obtain a cluster label for every image. We use the cluster labels as "pseudo supervision" for out-of-distribution (OOD) methods. Specifically, we finetune pretrained features on the task of classifying images by their cluster labels. We provide extensive analyses of our method and demonstrate the necessity of our two-stage approach. We evaluate it against the state-of-the-art selfsupervised and pretrained methods and demonstrate superior performance.

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Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representations

Cited by 3 publications

References 35 publications

Deep Industrial Image Anomaly Detection: A Survey

Deep Industrial Image Anomaly Detection: A Survey

Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors

Out-of-Distribution Detection without Class Labels

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