Semi-supervised Anomaly Detection

33 papers with code • 1 benchmarks • 2 datasets

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Libraries

Use these libraries to find Semi-supervised Anomaly Detection models and implementations

Most implemented papers

EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies

openvinotoolkit/anomalib 25 Mar 2023

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

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.

Deep Weakly-supervised Anomaly Detection

mala-lab/prenet 30 Oct 2019

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 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 3 Feb 2023

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).

ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognition

cool-xuan/imbalanced_sam ICCV 2023

To overcome this bottleneck, we leverage class priors to restrict the generalization scope of the class-agnostic SAM and propose a class-aware smoothness optimization algorithm named Imbalanced-SAM (ImbSAM).

Label-based Graph Augmentation with Metapath for Graph Anomaly Detection

missinghwan/mgad 21 Aug 2023

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.