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, robustness, and generalization resources. The repository contains a curated list of papers, tutorials, books, videos, articles and open-source libraries etc

Papers for Video Anomaly Detection, released codes collection, Performance Comparision.

The Official Repository for "Generalized OOD Detection: A Survey"

ADRepository: Real-world anomaly detection datasets, including tabular data (categorical and numerical data), time series data, graph data, image data, and video data.

👽 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

[ACM CSUR 2025] Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advances

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.

Outlier Exposure with Confidence Control for Out-of-Distribution Detection

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

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

DSP algorithms and utilities written in Rust. Performant, embedded friendly and no_std compatible.

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

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

A curated list of awesome resources dedicated to One Class Classification.

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.

Outlier detection related books and papers.

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

Code for ECML-PKDD 2022 Paper --- CMG: A Class-Mixed Generation Approach to Out-of-Distribution Detection