Semi-supervised Anomaly Detection
28 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Semi-supervised Anomaly Detection
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Libraries
Use these libraries to find Semi-supervised Anomaly Detection models and implementations
MVTec LOCO AD
UBI-Fights
Most implemented papers
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
We train a student network to predict the extracted features of normal, i. e., anomaly-free training images.
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.
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.
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 Weakly-supervised Anomaly Detection
mala-lab/prenet
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To detect both seen and unseen anomalies, we introduce a novel deep weakly-supervised approach, namely Pairwise Relation prediction Network (PReNet), that learns pairwise relation features and anomaly scores by predicting the relation of any two randomly sampled training instances, in which the pairwise relation can be anomaly-anomaly, anomaly-unlabeled, or unlabeled-unlabeled.
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.
Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks
tbw162/purigan
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When training on such datasets, existing GANs will learn a mixture distribution of desired and contaminated instances, rather than the desired distribution of desired data only (target distribution).
Label-based Graph Augmentation with Metapath for Graph Anomaly Detection
missinghwan/mgad
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To further efficiently exploit context information from metapath-based anomaly subgraph, we present a new framework, Metapath-based Graph Anomaly Detection (MGAD), incorporating GCN layers in both the dual-encoders and decoders to efficiently propagate context information between abnormal and normal nodes.
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.
