ssad 0.1.3

Framework for self-supervised training of reconstruction-based autoencoder models for anomaly detection.

SSAD — Self-Supervised Anomaly Detection Library

A Python library for autoencoder-based anomaly detection with self-supervised training and dynamic per-sample confidence updates.

Key Features

• Compute per-sample anomaly scores
• Estimate confidence from score distributions
• Recalibrate confidence intervals during training
• Apply confidence-aware losses (normal / abnormal / uncertain)
• Track experiments and artifacts with MLflow

Installation

pip install ssad

For development setup:

pip install -e .[dev]

Quick Links

• Repository: https://github.com/Orange-OpenSource/SSAD
• Examples: https://github.com/Orange-OpenSource/SSAD/tree/main/examples
• Issues: https://github.com/Orange-OpenSource/SSAD/issues

References

1. N. Najari, S. Berlemont, G. Lefebvre, S. Duffner, C. Garcia,
   Robust Variational Autoencoders and Normalizing Flows for Unsupervised Network Anomaly Detection,
   AINA 2022, doi: 10.1007/978-3-030-99587-4_24

2. N. Najari, S. Berlemont, G. Lefebvre, S. Duffner, C. Garcia,
   RADON: Robust Autoencoder for Unsupervised Anomaly Detection,
   SIN 2021, doi: 10.1109/SIN54109.2021.9699174