A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
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Updated
May 29, 2024 - 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
Deep Anomaly Detection with Outlier Exposure (ICLR 2019)
Benchmarking Generalized Out-of-Distribution Detection
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances (NeurIPS 2020)
A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization
ADRepository: Real-world anomaly detection datasets, including tabular data (categorical and numerical data), time series data, graph data, image data, and video data.
The Official Repository for "Generalized OOD Detection: A Survey"
Self-Supervised Learning for OOD Detection (NeurIPS 2019)
[TPAMI 2021] Adversarial Reciprocal Points Learning for Open Set Recognition
Papers for Open Knowledge Discovery
The Combined Anomalous Object Segmentation (CAOS) Benchmark
Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)
Official repository for the paper "Masksembles for Uncertainty Estimation" (CVPR 2021).
Deep Open Intent Classification with Adaptive Decision Boundary (AAAI 2021)
MASKER: Masked Keyword Regularization for Reliable Text Classification (AAAI 2021)
Feature Space Singularity for Out-of-Distribution Detection. (SafeAI 2021)
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.
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