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SIFT-SNN for Traffic-Flow Infrastructure Safety: A Real-Time Context-Aware Anomaly Detection Framework. [PDF]

J Imaging
Rathee M, Bačić B, Doborjeh M.
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AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection

International Conference on Learning Representations, 2023
Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliary data to detect anomalies without any training sample in a target dataset.
Qihang Zhou, Guansong Pang, Yu Tian, Shibo He, Jiming Chen   +4 more
semanticscholar   +1 more source

Revisiting Reverse Distillation for Anomaly Detection

Computer Vision and Pattern Recognition, 2023
Anomaly detection is an important application in large-scale industrial manufacturing. Recent methods for this task have demonstrated excellent accuracy but come with a latency trade-off.
Tran Dinh Tien, Anh Tuan Nguyen, Nguyen H. Tran, Ta Duc Huy, S. T. Duong, Chanh D. Tr, Nguyen, S. Q. Truong   +7 more
semanticscholar   +1 more source

TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data

Proceedings of the VLDB Endowment, 2022
Efficient anomaly detection and diagnosis in multivariate time-series data is of great importance for modern industrial applications. However, building a system that is able to quickly and accurately pinpoint anomalous observations is a challenging ...
Shreshth Tuli, G. Casale, N. Jennings
semanticscholar   +1 more source

MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection

Computer Vision and Pattern Recognition, 2019
The detection of anomalous structures in natural image data is of utmost importance for numerous tasks in the field of computer vision. The development of methods for unsupervised anomaly detection requires data on which to train and evaluate new ...
Paul Bergmann, Michael Fauser, David Sattlegger, C. Steger   +3 more
semanticscholar   +1 more source

PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization

ICPR Workshops, 2020
We present a new framework for Patch Distribution Modeling, PaDiM, to concurrently detect and localize anomalies in images in a one-class learning setting.
Thomas Defard, Aleksandr Setkov, Angélique Loesch, Romaric Audigier   +3 more
semanticscholar   +1 more source

Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network

Knowledge Discovery and Data Mining, 2019
Industry devices (i.e., entities) such as server machines, spacecrafts, engines, etc., are typically monitored with multivariate time series, whose anomaly detection is critical for an entity's service quality management.
Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun, Dan Pei   +5 more
semanticscholar   +1 more source

AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection

European Conference on Computer Vision
Zero-shot anomaly detection (ZSAD) targets the identification of anomalies within images from arbitrary novel categories. This study introduces AdaCLIP for the ZSAD task, leveraging a pre-trained vision-language model (VLM), CLIP.
Yunkang Cao, Jiangning Zhang, Luca Frittoli, Yuqi Cheng, Weiming Shen, Giacomo Boracchi   +5 more
semanticscholar   +1 more source

Anomaly Detection

Computer Fraud and Security, 2019
Due to the significance it holds in the concept of fraud, security in computers and business, anomaly detection serves very much purpose. Using techniques in unsupervised machine learning, the two algorithms applied in this study are Isolation Forest and Autoencoder in credit card fraud detection in financial datasets.
Gopinath Rebala, Ajay Ravi, Sanjay Churiwala   +2 more
openaire   +2 more sources

Anomaly Detection in Time Series: A Comprehensive Evaluation

Proceedings of the VLDB Endowment, 2022
Detecting anomalous subsequences in time series data is an important task in areas ranging from manufacturing processes over finance applications to health care monitoring.
Sebastian Schmidl, Phillip Wenig, Thorsten Papenbrock   +2 more
semanticscholar   +1 more source