A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
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Updated
Jul 2, 2024 - Python
A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
Benchmarking Generalized Out-of-Distribution Detection
fastdup is a powerful free tool designed to rapidly extract valuable insights from your image & video datasets. Assisting you to increase your dataset images & labels quality and reduce your data operations costs at an unparalleled scale.
Source code for Skip-GANomaly paper
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances (NeurIPS 2020)
Latent space autoregression for novelty detection.
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
👽 Out-of-Distribution Detection with PyTorch
Open-set Recognition 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.
A scikit-learn compatible library for anomaly 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".
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.
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.
Code for paper entitled "Learning to detect RFI in radio astronomy without seeing it"
[WACV'23] Mixture Outlier Exposure for Out-of-Distribution Detection in Fine-grained Environments
MICCAI 2021 | Adversarial based selective network for unsupervised anomaly segmentation
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.
A Variational AutoEncoder implemented with Keras and used to perform Novelty Detection with the EMNIST-Letters Dataset.
Implementation of q-Space Novelty Detection with Variational Autoencoders
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