Latent Outlier Exposure for Anomaly Detection with Contaminated Data

Qiu, Chen; Li, Aodong; Kloft, Marius; Rudolph, M.; Mandt, Stephan

doi:10.48550/arxiv.2202.08088

Cited by 3 publications

(3 citation statements)

References 18 publications

(28 reference statements)

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“…Yoon et al [131] use a data refinement approach to improve the robustness of one-class classification model. Qiu et al [132] propose a strategy for training an anomaly detector in the presence of unlabeled anomalies, which is compatible with a broad class of models. They create labelled anomalies synthetically and jointly optimize the loss function with normal data and synthesis abnormal data.…”

Section: Noisy Anomaly Detectionmentioning

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: Noisy Anomaly Detectionmentioning

confidence: 99%

Deep Industrial Image Anomaly Detection: A Survey

Liu¹,

Xie²,

Wang³

et al. 2023

Preprint

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“…Anomaly detection (AD), which is also known as outlier detection, is a key machine learning (ML) task with numerous applications, including anti-money laundering [57], rare disease detection [116], social media analysis [110,114], and intrusion detection [54]. AD algorithms aim to identify data instances that deviate significantly from the majority of data objects [35,82,87,96], and numerous methods have been developed in the last few decades [2,53,64,65,76,93,104,118]. Among them, majority are designed for tabular data (i.e., no time dependency and graph structure).…”

Section: Introductionmentioning

confidence: 99%

ADBench: Anomaly Detection Benchmark

Han¹,

Hu²,

Huang³

et al. 2022

Preprint

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Given a long list of anomaly detection algorithms developed in the last few decades, how do they perform with regard to (i) varying levels of supervision, (ii) different types of anomalies, and (iii) noisy and corrupted data? In this work, we answer these key questions by conducting (to our best knowledge) the most comprehensive anomaly detection benchmark with 30 algorithms on 55 benchmark datasets, named ADBench. Our extensive experiments (93,654 in total) identify meaningful insights into the role of supervision and anomaly types, and unlock future directions for researchers in algorithm selection and design. With ADBench, researchers can easily conduct comprehensive and fair evaluations for newly proposed methods on the datasets (including our contributed ones from natural language and computer vision domains) against the existing baselines. To foster accessibility and reproducibility, we fully open-source ADBench and the corresponding results.

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“…In addition to the basic image classification task that aims at identifying abnormal visual samples from the base class [59,14], anomaly detection is also widely exploited in other fields, such as defect detection [1,21,9,27] and abnormal event detection [34,10,64,37]. Some works focus on designing anomaly scores and anomaly classifiers, such as [43,47]. Some methods combine reinforcement learning [44] and knowledge distillation [38] in the anomaly detection.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection

Wang¹,

Zhan²,

Wang³

et al. 2022

Preprint

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Anomaly detection aims at identifying deviant samples from the normal data distribution. Contrastive learning has provided a successful way to sample representation that enables effective discrimination on anomalies. However, when contaminated with unlabeled abnormal samples in training set under semi-supervised settings, current contrastive-based methods generally 1) ignore the comprehensive relation between training data, leading to suboptimal performance, and 2) require fine-tuning, resulting in low efficiency. To address the above two issues, in this paper, we propose a novel hierarchical semi-supervised contrastive learning (HSCL) framework, for contamination-resistant anomaly detection. Specifically, HSCL hierarchically regulates three complementary relations: sample-to-sample, sample-to-prototype, and normal-to-abnormal relations, enlarging the discrimination between normal and abnormal samples with a comprehensive exploration of the contaminated data. Besides, HSCL is an end-to-end learning approach that can efficiently learn discriminative representations without fine-tuning. HSCL achieves state-of-the-art performance in multiple scenarios, such as one-class classification and cross-dataset detection. Extensive ablation studies further verify the effectiveness of each considered relation. The code is available at https://github.com/GaoangW/HSCL.

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Latent Outlier Exposure for Anomaly Detection with Contaminated Data

Cited by 3 publications

References 18 publications

Deep Industrial Image Anomaly Detection: A Survey

Deep Industrial Image Anomaly Detection: A Survey

ADBench: Anomaly Detection Benchmark

Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection

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