alibi-detect is an open source Python library focused on outlier, adversarial and concept drift detection. The package aims to cover both online and offline detectors for tabular data, images and time series. The outlier detection methods should allow the user to identify global, contextual and collective outliers.
- Mahalanobis Outlier Detector
- Isolation Forest Outlier Detector
- Variational Auto-Encoder Outlier Detector
- Auto-Encoder Outlier Detector
- Variational Auto-Encoding Gaussian Mixture Model Outlier Detector
- Auto-Encoding Gaussian Mixture Model Outlier Detector
- Prophet Outlier Detector
- Spectral Residual Outlier Detector
- Sequence-to-Sequence (Seq2Seq) Outlier Detector
- Variational Auto-Encoder Adversarial Detector
- Mahalanobis outlier detection on KDD Cup ‘99 dataset
- Isolation Forest outlier detection on KDD Cup ‘99 dataset
- VAE outlier detection on KDD Cup ‘99 dataset
- VAE outlier detection on CIFAR10
- AE outlier detection on CIFAR10
- AEGMM and VAEGMM outlier detection on KDD Cup ‘99 dataset
- Time-series outlier detection using Prophet on weather data
- Time series outlier detection with Spectral Residuals on synthetic data
- Time series outlier detection with Seq2Seq models on synthetic data
- Seq2Seq time series outlier detection on ECG data
- Adversarial VAE detection on MNIST