Models for Tabular Data

Unsupervised Models

implemented unsupervised anomaly detection models

models.DeepSVDD	Deep One-class Classification for Anomaly Detection (ICML'18)
models.RCA	A Deep Collaborative Autoencoder Approach for Anomaly Detection (IJCAI'21)
models.DevNet	Deviation Networks for Weakly-supervised Anomaly Detection (KDD'19) [BPSvdH19]
models.DeepIsolationForest	Deep Isolation Forest for Anomaly Detection
models.REPEN	Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection (KDD'18) []
models.SLAD	Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning (ICML'23)
models.ICL	Anomaly Detection for Tabular Data with Internal Contrastive Learning
models.RDP	Unsupervised Representation Learning by Predicting Random Distances (IJCAI'20)
models.GOAD	Classification-Based Anomaly Detection for General Data (ICLR'20)
models.NeuTraL	Neural Transformation Learning-based Anomaly Detection (ICML'21)

Weakly-supervised Models

implemented weakly-sueprvised anomaly detection models

models.DevNet	Deviation Networks for Weakly-supervised Anomaly Detection (KDD'19) [BPSvdH19]
models.DeepSAD	Deep Semi-supervised Anomaly Detection (ICLR'20)
models.FeaWAD	Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection (TNNLS'21)
models.RoSAS	RoSAS: Deep semi-supervised anomaly detection with contamination-resilient continuous supervision (IP&M'23)
models.PReNet	Deep Weakly-supervised Anomaly Detection (KDD‘23)

References

[BPSvdH19] (1,2,3,4)

Guansong Pang, Chunhua Shen, and Anton van den Hengel. Deep anomaly detection with deviation networks. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 353–362. 2019.

[BRVG+18]

Lukas Ruff, Robert Vandermeulen, Nico Görnitz, Lucas Deecke, Shoaib Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft. Deep one-class classification. International conference on machine learning, 2018.