Anomaly Classification
12 papers with code • 0 benchmarks • 1 datasets
Benchmarks
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ELPV
Most implemented papers
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
caoyunkang/WinClip
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CVPR 2023
Visual anomaly classification and segmentation are vital for automating industrial quality inspection.
BINet: Multi-perspective Business Process Anomaly Classification
tnolle/binet
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Finally, we demonstrate that a simple set of rules can be used to utilize the output of BINet for anomaly classification.
APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD
In this challenge, our method achieved first place in the zero-shot track, especially excelling in segmentation with an impressive F1 score improvement of 0. 0489 over the second-ranked participant.
Fence GAN: Towards Better Anomaly Detection
However, the traditional GAN loss is not directly aligned with the anomaly detection objective: it encourages the distribution of the generated samples to overlap with the real data and so the resulting discriminator has been found to be ineffective as an anomaly detector.
Power System Anomaly Detection and Classification Utilizing WLS-EKF State Estimation and Machine Learning
This paper presents a new algorithm for detecting anomaly presence, classifying the anomaly type and identifying the origin of the anomaly, i. e., measurements that contain gross errors in case of bad data, or buses associated with loads experiencing a sudden change, or state variables targeted by false data injection attack.
Component-aware anomaly detection framework for adjustable and logical industrial visual inspection
liutongkun/comad
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Meanwhile, segmenting a product image into multiple components provides a novel perspective for industrial visual inspection, demonstrating great potential in model customization, noise resistance, and anomaly classification.
A Prototype-Based Neural Network for Image Anomaly Detection and Localization
98chao/ProtoAD
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This paper proposes ProtoAD, a prototype-based neural network for image anomaly detection and localization.
Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI
svrtk/masc-multi-task-segmentation-and-classification
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Adding a classifier improves the anatomical and topological accuracy of all correctly classified double aortic arch subjects.
Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound Diagnosis
datct00/Beyond-Traditional-Approaches-Multi-Task-Network-for-Breast-Ultrasound-Diagnosis
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Breast Ultrasound plays a vital role in cancer diagnosis as a non-invasive approach with cost-effective.
MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images
xrli-U/MuSc
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We reveal that the abundant normal and abnormal cues implicit in unlabeled test images can be exploited for anomaly determination, which is ignored by prior methods.
