Semi-supervised Anomaly Detection

14 papers with code • 1 benchmarks • 1 datasets

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Most implemented papers

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

openvinotoolkit/anomalib 17 May 2018

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

WaqasSultani/AnomalyDetectionCVPR2018 CVPR 2018

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.

Deep Semi-Supervised Anomaly Detection

lukasruff/Deep-SAD-PyTorch 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 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 9 Jan 2018

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 29 Oct 2018

Security surveillance is critical to social harmony and people's peaceful life.

Semi-supervised Anomaly Detection using AutoEncoders

msminhas93/anomaly-detection-using-autoencoders 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

tuananh0305/GCN_ANOMALY_DETECTION 27 Feb 2020

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

Fraunhofer-AISEC/A3 3 Mar 2020

Based on the activation values in the target network, the alarm network decides if the given sample is normal.