Deep learning for unsupervised anomaly localization in industrial images: A survey
X Tao, X Gong, X Zhang, S Yan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Currently, deep learning-based visual inspection has been highly successful with the help of
supervised learning methods. However, in real industrial scenarios, the scarcity of defect …
supervised learning methods. However, in real industrial scenarios, the scarcity of defect …
Deep industrial image anomaly detection: A survey
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 …
anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning …
Pad: A dataset and benchmark for pose-agnostic anomaly detection
Object anomaly detection is an important problem in the field of machine vision and has
seen remarkable progress recently. However, two significant challenges hinder its research …
seen remarkable progress recently. However, two significant challenges hinder its research …
Semi-supervised noise-resilient anomaly detection with feature autoencoder
Most methods only use normal samples to learn anomaly detection (AD) models in an
unsupervised manner. However, these samples may be noisy in real-world applications …
unsupervised manner. However, these samples may be noisy in real-world applications …
[HTML][HTML] Can we detect plant diseases without prior knowledge of their existence?
There is a need to help farmers make decisions to maximize crop yields. Many studies have
emerged in recent years using deep learning on remotely sensed images to detect plant …
emerged in recent years using deep learning on remotely sensed images to detect plant …
ALLO: A Photorealistic Dataset and Data Generation Pipeline for Anomaly Detection During Robotic Proximity Operations in Lunar Orbit
S Leveugle, CW Lee, S Stolpner, C Langley… - arXiv preprint arXiv …, 2024 - arxiv.org
NASA's forthcoming Lunar Gateway space station, which will be uncrewed most of the time,
will need to operate with an unprecedented level of autonomy. Enhancing autonomy on the …
will need to operate with an unprecedented level of autonomy. Enhancing autonomy on the …
Cross-Modal Distillation in Industrial Anomaly Detection: Exploring Efficient Multi-Modal IAD
W Sui, D Lichau, J Lefèvre, H Phelippeau - arXiv preprint arXiv …, 2024 - arxiv.org
Recent studies of multi-modal Industrial Anomaly Detection (IAD) based on point clouds and
RGB images indicated the importance of exploiting redundancy and complementarity …
RGB images indicated the importance of exploiting redundancy and complementarity …
Deep adaptive anomaly detection using an active learning framework
E Sekyi - 2022 - open.uct.ac.za
Anomaly detection is the process of finding unusual events in a given dataset. Anomaly
detection is often performed on datasets with a fixed set of predefined features. As a result of …
detection is often performed on datasets with a fixed set of predefined features. As a result of …