# Benchmarks Add a Result

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# GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

17 May 2018samet-akcay/ganomaly

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).

575

# 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.

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# Deep Semi-Supervised Anomaly Detection

Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets.

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# Learning Temporal Regularity in Video Sequences

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.

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# Abnormal Event Detection in Videos using Spatiotemporal Autoencoder

6 Jan 2017drsagitn/anomaly-detection-and-localization

We present an efficient method for detecting anomalies in videos.

18

# Semi-supervised Anomaly Detection using AutoEncoders

But for defect detection lack of availability of a large number of anomalous instances and labelled data is a problem.

17

# Generative Neural Networks for Anomaly Detection in Crowded Scenes

29 Oct 2018tianwangbuaa/VAE-for-abnormal-event-detection

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

9

# Iterative weak/self-supervised classification framework for abnormal events detection

3 Jan 2021DegardinBruno/human_self_learning_anomaly

The detection of abnormal events in surveillance footage remains a challenge and has been the scope of various research works.

5

# $\text{A}^3$: Activation Anomaly Analysis

3 Mar 2020Fraunhofer-AISEC/A3

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

5

# Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rays

Detecting anomalies in musculoskeletal radiographs is of paramount importance for large-scale screening in the radiology workflow.

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