/WSAD

A Collection of Resources for Weakly-supervised Anomaly Detection (WSAD)

Primary LanguagePythonMIT LicenseMIT

Weakly-supervised Anomaly Detection: A Survey

This repo is constructed for collecting and categorizing papers about weakly supervised anomaly detection models according to our survey paper——Weakly Supervised Anomaly Detection: A Survey

Summary and categorization of weakly supervised anomaly detection (WSAD) algorithms

We first summarize and further categorize existing WSAD algorithms into three categories, including: (i) incomplete supervision; (ii) inexact supervision; (iii) inaccurate supervision

1.Summary of WSAD Algorithms

Method Reference Venue Backbone Modalities Key Idea Official Code
Incomplete Supervision
OE ref KDD'14 - Tabular Anomaly feature representation learning ×
XGBOD ref IJCNN'18 - Tabular Anomaly feature representation learning √
DeepSAD ref ICLR'20 MLP Tabular Anomaly feature representation learning √
ESAD ref Preprint MLP Tabular Anomaly feature representation learning ×
DSSAD ref ICASSP'21 CNN Image/Video Anomaly feature representation learning ×
REPEN ref KDD'18 MLP Tabular Anomaly feature representation learning ×
AA-BiGAN ref IJCAI'22 GAN Tabular Anomaly feature representation learning √
Dual-MGAN ref TKDD'22 GAN Tabular Anomaly feature representation learning √
DevNet ref KDD'19 MLP Tabular Anomaly score learning √
PReNet ref Preprint MLP Tabular Anomaly score learning ×
FEAWAD ref TNNLS'21 AE Tabular Anomaly score learning √
SNARE ref KDD'09 - Graph Graph learning and label propagation ×
AESOP ref KDD'14 - Graph Graph learning and label propagation ×
SemiGNN ref ICDM'19 MLP+Attention Graph Graph learning and label propagation ×
SemiGAD ref IJCNN'21 GNN Graph Graph learning and label propagation ×
Meta-GDN ref WWW'21 GNN Graph Graph learning and label propagation √
SemiADC ref IS Journal'21 GAN Graph Graph learning and label propagation ×
SSAD ref JAIR'13 - Tabular Active learning ×
AAD ref ICDM'16 - Tabular Active learning √
SLA-VAE ref WWW'22 VAE Time series Active learning ×
Meta-AAD ref ICDM'20 MLP Tabular Reinforcement learning √
DPLAN ref KDD'21 MLP Tabular Reinforcement learning ×
GraphUCB ref WSDM'19 - Graph Reinforcement learning √
Inexact Supervision
MIL ref CVPR'18 MLP Video Multiple Instance Learning √
TCN-IBL ref ICIP'19 CNN Video Multiple Instance Learning ×
AR-Net ref ICME'20 MLP Video Multiple Instance Learning √
RTFM ref ICCV'21 CNN+Attention Video Multiple Instance Learning √
Motion-Aware ref BMVC'19 AE+Attention Video Multiple Instance Learning ×
CRF-Attention ref ICCV'21 TRN+Attention Video Multiple Instance Learning ×
MPRF ref IJCAI'21 MLP+Attention Video Multiple Instance Learning ×
MCR ref ICME'22 MLP+Attention Video Multiple Instance Learning ×
XEL ref SPL'21 MLP Video Cross-epoch Learning √
MIST ref CVPR'21 MLP+Attention Video Multiple Instance Learning √
MSLNet ref AAAI'22 Transformer Video Multiple Instance Learning √
SRF ref SPL'20 MLP Video Self Reasoning ×
WETAS ref ICCV'21 MLP Time-series/Video Dynamic Time Warping ×
Inexact AUC ref ML Journal'20 AE Tabular AUC maximization ×
Isudra ref TIST'21 - Time-series Bayesian optimization √
Inaccurate Supervision
LAC ref CIKM'21 MLP/GBDT Tabular Ensemble learning ×
ADMoE ref AAAI'23 Agnostic Tabular Ensemble learning √
BGPAD ref ICNP'21 LSTM+Attention Time series Denoising network √
SemiADC ref IS Journal'21 GAN Graph Denoising network ×
TSN ref CVPR'19 GCN Video GCN √

2.Categorization of WSAD algorithms

2.1 AD with Incomplete Supervision

2.2 AD with Inexact Supervision

2.3 AD with Inaccurate Supervision

Experiment

One can easily reproduce the experimental results in our paper by running the run.py python file in the experiments folder.

Method $\gamma_{l}=1$% $\gamma_{l}=5$% $\gamma_{l}=25$% $\gamma_{l}=50$%
AUC-ROC
XGBOD 80.03 86.68 93.20 95.28
DeepSAD 75.25 81.74 89.64 92.72
REPEN 77.20 82.23 86.26 87.45
DevNet 79.05 85.94 89.76 90.97
PReNet 79.04 85.66 89.88 91.11
FEAWAD 73.93 82.44 89.20 91.55
AUC-PR
XGBOD 46.23 61.58 75.89 80.57
DeepSAD 38.06 49.65 67.04 74.47
REPEN 46.57 56.38 63.39 65.73
DevNet 53.61 64.01 69.52 71.13
PReNet 54.52 64.19 70.46 71.62
FEAWAD 51.19 62.30 69.65 72.34