Skip to content

GuansongPang/SOTA-Deep-Anomaly-Detection

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

13 Commits
 
 

Repository files navigation

Implementation of SOTA Deep Anomaly Detection Methods

In this repository, we provide a continuously updated collection of implementation of SOTA deep anomaly detection methods in the literature. This list was originally collected and presented in our CSUR survey paper on deep anomaly detection. This repository is created to serve as an extension to that list. You may cite the survey paper below to acknolwedge our contribution.

@article{pang2021deep,
  title={Deep learning for anomaly detection: A review},
  author={Pang, Guansong and Shen, Chunhua and Cao, Longbing and Hengel, Anton Van Den},
  journal={ACM Computing Surveys (CSUR)},
  volume={54},
  number={2},
  pages={1--38},
  year={2021},
  publisher={ACM New York, NY, USA}
}

Algorithms and Source Codes

Method Publication Venue Year API Link Supervision* Data
RDA KDD 2017 Tensorflow https://git.io/JfYG5 Semi-supervised Image
AnoGAN IPMI 2017 Tensorflow https://git.io/JfGgc Semi-supervised Image
Fast AntoGAN Medical Image Analysis 2019 Tensorflow https://git.io/JfZRn Semi-supervised Image
EBGAN arXiv 2018 Keras https://git.io/JfGgG Semi-supervised Image
ALAD ICDM 2018 Keras https://git.io/JfZ8v Semi-supervised Image
GANomaly ACCV 2018 PyTorch https://git.io/JfGgn Semi-supervised Image
GT NeurIPS 2018 Keras https://git.io/JfZRW Semi-supervised Image
OC-NN arXiv 2018 Keras https://git.io/JfGgZ Semi-supervised Image
Deep SVDD ICML 2018 Tensorflow https://git.io/JfZRR Semi-supervised Image
Deep SAD ICLR 2020 PyTorch https://git.io/JfOkr Weakly-supervised Image
DAGMM ICLR 2018 PyTorch https://git.io/JfZR0 Unsupervised Image
ALOCC CVPR 2018 Tensorflow https://git.io/Jf4p4 Semi-supervised Image
LSA CVPR 2019 Torch https://git.io/Jf4pW Semi-supervised Image
E3Outlier NeurIPS 2019 PyTorch https://git.io/Jf4pl Unsupervised Image
OCGAN CVPR 2019 MXNet https://git.io/Jf4p0 Semi-supervised Image
CCD MICCAI 2021 PyTorch https://git.io/JKnEM Semi-supervised Image
DevNet arXiv 2021 PyTorch https://git.io/DevNet Weakly-supervised Image
IGD AAAI 2022 PyTorch https://git.io/JMj7N Semi-supervised Image
MemAE ICCV 2019 PyTorch https://git.io/JVnlz Semi-supervised Image&Video
FFP CVPR 2018 Tensorflow https://git.io/Jf4pc Semi-supervised Video
MIL CVPR 2018 Keras https://git.io/JfZRz Weakly-supervised Video
GCLNC CVPR 2019 PyTorch https://git.io/JwoHS Weakly-supervised Video
RTFM ICCV 2021 PyTorch https://git.io/JKnE6 Weakly-supervised Video
OCAN AAAI 2019 Tensorflow https://git.io/JfYGb Semi-supervised Sequential
OmniAnomaly KDD 2019 Tensorflow https://git.io/JKnu4 Unsupervised Time series
REPEN KDD 2018 Keras https://git.io/JfZRg Unsupervised Tabular
AE-1SVM ECML-PKDD 2018 Tensorflow https://git.io/JfGgl Unsupervised Tabular
DevNet KDD 2019 Keras https://git.io/JfZRw Weakly-supervised Tabular
RDP IJCAI 2020 PyTorch https://git.io/RDP Unsupervised Tabular
A3 ECMLPKDD 2020 Keras https://git.io/JM0I1 Weakly-supervised Tabular
FenceGAN arXiv 2019 Keras https://git.io/Jf4pR Semi-supervised Image&Tabular
GCN-AE SDM 2019 PyTorch https://git.io/JVn43 Unsupervised Graph
CoLA TNNLS 2021 PyTorch https://git.io/Jy0b3 Unsupervised Graph
GLocalKD WSDM 2022 PyTorch https://git.io/GLocalKD Semi/Un-supervised Graph

* In the supervision column, 'semi-supervised' indicates that the specific methods are trained on exclusively normal data, 'unsupervised' indicates that they are trained on fully unlabeled data (mostly normal data), while `weakly-supervised' indicates that the methods use some form of weak supervision, e.g., coarse class labels, or partially observed anomaly class labels

Releases

No releases published

Packages

No packages published