A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques

fastdup is a powerful, free tool designed to rapidly generate valuable insights from image and video datasets. It helps enhance the quality of both images and labels, while significantly reducing data operation costs, all with unmatched scalability.

Benchmarking Generalized Out-of-Distribution Detection

👽 Out-of-Distribution Detection with PyTorch

CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances (NeurIPS 2020)

Latent space autoregression for novelty detection.

Source code for Skip-GANomaly paper

Generative Probabilistic Novelty Detection with Adversarial Autoencoders

A project to add scalable state-of-the-art out-of-distribution detection (open set recognition) support by changing two lines of code! Perform efficient inferences (i.e., do not increase inference time) and detection without classification accuracy drop, hyperparameter tuning, or collecting additional data.

This is the official repository for the paper "A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges".

A project to improve out-of-distribution detection (open set recognition) and uncertainty estimation by changing a few lines of code in your project! Perform efficient inferences (i.e., do not increase inference time) without repetitive model training, hyperparameter tuning, or collecting additional data.

Open-set Recognition with Adversarial Autoencoders

A scikit-learn compatible library for anomaly detection

[WACV'23] Mixture Outlier Exposure for Out-of-Distribution Detection in Fine-grained Environments

Locally hosted AI Agent Python Tool To Generate Novel Research Hypothesis + Titles + Abstracts

[IEEE TII 2025] Official Implementation for "Dual-Detector Reoptimization for Federated Weakly Supervised Video Anomaly Detection via Adaptive Dynamic Recursive Mapping"

A project to train your model from scratch or fine-tune a pretrained model using the losses provided in this library to improve out-of-distribution detection and uncertainty estimation performances. Calibrate your model to produce enhanced uncertainty estimations. Detect out-of-distribution data using the defined score type and threshold.

Python package to accelerate research on generalized out-of-distribution (OOD) detection.

Novelty detection for data streams in Python

MICCAI 2021 | Adversarial based selective network for unsupervised anomaly segmentation