A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
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Updated
Jul 19, 2023 - Python
A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
A curated list of papers & resources linked to open set recognition, out-of-distribution, open set domain adaptation and open world recognition
Benchmarking Generalized Out-of-Distribution Detection
Deep Anomaly Detection with Outlier Exposure (ICLR 2019)
The Official Repository for "Generalized OOD Detection: A Survey"
A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization
Self-Supervised Learning for OOD Detection (NeurIPS 2019)
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances (NeurIPS 2020)
ADRepository: Real-world anomaly detection datasets, including tabular data (categorical and numerical data), time series data, graph data, image data, and video data.
The Combined Anomalous Object Segmentation (CAOS) Benchmark
The Ultimate Reference for Out of Distribution Detection with Deep Neural Networks
Papers for Open Knowledge Discovery
[TPAMI 2021] Adversarial Reciprocal Points Learning for Open Set Recognition
Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)
Official repository for the paper "Masksembles for Uncertainty Estimation" (CVPR2021).
Feature Space Singularity for Out-of-Distribution Detection. (SafeAI 2021)
Deep Open Intent Classification with Adaptive Decision Boundary (AAAI 2021)
Outlier Exposure with Confidence Control for Out-of-Distribution Detection
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