Algorithm Overview

The following tables summarize the advised use cases for the current algorithms. Please consult the method specific pages for a more detailed breakdown of each method. The column Feature Level indicates whether the detection can be done and returned at the feature level, e.g. per pixel for an image.

Outlier Detection

Detector	Tabular	Image	Time Series	Text	Categorical Features	Online	Feature Level
Isolation Forest	✔				✔
Mahalanobis Distance	✔				✔	✔
AE	✔	✔					✔
VAE	✔	✔					✔
AEGMM	✔	✔
VAEGMM	✔	✔
Likelihood Ratios	✔	✔	✔		✔		✔
Prophet			✔
Spectral Residual			✔			✔	✔
Seq2Seq			✔				✔

Adversarial Detection

Detector	Tabular	Image	Time Series	Text	Categorical Features	Online	Feature Level
Adversarial AE	✔	✔
Model distillation	✔	✔	✔	✔	✔

Drift Detection

Detector	Tabular	Image	Time Series	Text	Categorical Features	Online	Feature Level
Kolmogorov-Smirnov	✔	✔		✔	✔		✔
Cramér-von Mises	✔	✔				✔	✔
Fisher’s Exact Test	✔				✔	✔	✔
Least-Squares Density Difference	✔	✔		✔	✔	✔
Maximum Mean Discrepancy (MMD)	✔	✔		✔	✔	✔
Learned Kernel MMD	✔	✔	✔	✔
Context-aware MMD	✔	✔	✔	✔	✔
Chi-Squared	✔				✔		✔
Mixed-type tabular	✔				✔		✔
Classifier	✔	✔	✔	✔	✔
Spot-the-diff	✔	✔	✔	✔	✔		✔
Classifier Uncertainty	✔	✔	✔	✔	✔
Regressor Uncertainty	✔	✔	✔	✔	✔

All drift detectors and built-in preprocessing methods support both PyTorch and TensorFlow backends. The preprocessing steps include randomly initialized encoders, pretrained text embeddings to detect drift on using the transformers library and extraction of hidden layers from machine learning models. The preprocessing steps allow to detect different types of drift such as covariate and predicted distribution shift.