There are 9 repositories under novelty-detection topic.
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.
[WACV'23] Mixture Outlier Exposure for Out-of-Distribution Detection in Fine-grained Environments
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.
DSP algorithms and utilities written in Rust. Performant, embedded friendly and no_std compatible.
A curated list of awesome resources dedicated to One Class Classification.
Outlier detection related books and papers.
Python package to accelerate research on generalized out-of-distribution (OOD) detection.
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
A Variational AutoEncoder implemented with Keras and used to perform Novelty Detection with the EMNIST-Letters Dataset.
Code for paper entitled "Learning to detect RFI in radio astronomy without seeing it"
This project, proposes a methodology for continuous implicit authentication of smartphones users, using the navigation data, in order to improve the security and ensure the privacy of sensitive personal data.
Density Forests for Uncertainty, SIE Master Project, EPFL, Spring Semester 2018
Python implementation of the MINAS novelty detection algorithm for data streams.