Semi-supervised Anomaly Detection
14 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Semi-supervised Anomaly Detection
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UBI-Fights
Most implemented papers
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal).
Real-world Anomaly Detection in Surveillance Videos
To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled training videos, i. e. the training labels (anomalous or normal) are at video-level instead of clip-level.
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
ShrishtiHore/Anomaly-Detection-in-CCTV-Surveillance-Videos
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We present an efficient method for detecting anomalies in videos.
Deep Semi-Supervised Anomaly Detection
lukasruff/Deep-SAD-PyTorch
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ICLR 2020
Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets.
Learning Temporal Regularity in Video Sequences
tnybny/Frame-level-anomalies-in-videos
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CVPR 2016
Perceiving meaningful activities in a long video sequence is a challenging problem due to ambiguous definition of 'meaningfulness' as well as clutters in the scene.
An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos
santiagxf/ContrastiveLearning
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Videos represent the primary source of information for surveillance applications and are available in large amounts but in most cases contain little or no annotation for supervised learning.
Generative Neural Networks for Anomaly Detection in Crowded Scenes
tianwangbuaa/VAE-for-abnormal-event-detection
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Security surveillance is critical to social harmony and people's peaceful life.
Semi-supervised Anomaly Detection using AutoEncoders
msminhas93/anomaly-detection-using-autoencoders
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Journal of Computational Vision and Imaging Systems 2020
But for defect detection lack of availability of a large number of anomalous instances and labelled data is a problem.
Semi-supervised Anomaly Detection on Attributed Graphs
To learn node embeddings specialized for anomaly detection, in which there is a class imbalance due to the rarity of anomalies, the parameters of a GCN are trained to minimize the volume of a hypersphere that encloses the node embeddings of normal instances while embedding anomalous ones outside the hypersphere.
$\text{A}^3$: Activation Anomaly Analysis
Based on the activation values in the target network, the alarm network decides if the given sample is normal.
