Zero-shot anomaly detection via batch normalization

A Li, C Qiu, M Kloft, P Smyth… - Advances in Neural …, 2023 - proceedings.neurips.cc
Anomaly detection (AD) plays a crucial role in many safety-critical application domains. The
challenge of adapting an anomaly detector to drift in the normal data distribution, especially …

Anomalous sound detection using audio representation with machine ID based contrastive learning pretraining

J Guan, F **ao, Y Liu, Q Zhu… - ICASSP 2023-2023 IEEE …, 2023 - ieeexplore.ieee.org
Existing contrastive learning methods for anomalous sound detection refine the audio
representation of each audio sample by using the contrast between the samples' …

[HTML][HTML] Regularized contrastive masked autoencoder model for machinery anomaly detection using diffusion-based data augmentation

E Zahedi, M Saraee, FS Masoumi, M Yazdinejad - Algorithms, 2023 - mdpi.com
Unsupervised anomalous sound detection, especially self-supervised methods, plays a
crucial role in differentiating unknown abnormal sounds of machines from normal sounds …

Machine anomalous sound detection based on audio synthesis generative adversarial network

T Cheng, F Guo - Journal of Physics: Conference Series, 2024 - iopscience.iop.org
In the pursuit of implementing safety monitoring within Industry 4.0 smart production, deep
learning methodologies are employed to harness the audio signals emanating from the …

[КНИГА][B] Deep Anomaly Detection and Distribution Shifts

A Li - 2024 - search.proquest.com
Anomaly detection is important in various applications, from cyber-security, transportation,
industry, and finance to healthcare. The anomaly detection problem is to identify anomalies …