异常检测相关资源大列表 https://github.com/zhuyiche/awesome-anomaly-detection
应该是当下最全面的「Python异常检测工具库」了。除了收到近500个Star以外，我们还写了一篇JMLR（在审）。最近一直在加紧扩充算法和优化效率（主要是通过joblib和numba），这两周写了SOS、LOCI、LSCP等算法，同时还有两个全新的深度学习异常检测即将上线，敬请期待。 https://github.com/yzhao062/pyod
新书：Python深度学习异常检测(Keras/PyTorch) https://www.amazon.com/Beginning-Anomaly-Detection-Python-Based-Learning/dp/1484251768
【Keras/TensorFlow异常检测】《Anomaly detection with Keras, TensorFlow, and Deep Learning | PyImageSearch》 https://www.pyimagesearch.com/2020/03/02/anomaly-detection-with-keras-tensorflow-and-deep-learning/
深度学习缺陷检测 https://github.com/sundyCoder/DEye
Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning https://github.com/maxkferg/metal-defect-detection
A Benchmark for Anomaly Segmentation https://github.com/hendrycks/anomaly-seg
AI与小数据：制造业罕见缺陷的自动检测策略 https://www.industryweek.com/technology-and-iiot/digital-tools/article/21122846/making-ai-work-with-small-data
Code for the CVPR'19 paper "Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos" https://github.com/RomeroBarata/skeleton_based_anomaly_detection
https://github.com/raghavchalapathy/oc-nn
An Acceleration System for Large-Scale Unsupervised Anomaly Detection https://github.com/yzhao062/SUOD
（最先进的缺陷检测网络） A Tensorflow implementation of "Segmentation-Based Deep-Learning Approach for Surface-Defect Detection"
https://github.com/Wslsdx/Deep-Learning-Approach-for-Surface-Defect-Detection
Cork/Face Presentation Attack Detection https://github.com/ee09115/spoofing_detection
A PyTorch implementation of the Deep SVDD anomaly detection method https://github.com/lukasruff/Deep-SVDD-PyTorch
This is the official implementation of "Deep Anomaly Detection Using Geometric Transformations" https://github.com/izikgo/AnomalyDetectionTransformations
A Survey on GANs for Anomaly Detection https://arxiv.org/abs/1906.11632
布匹缺陷检测 https://github.com/GT-ZhangAcer/Fabric_Defect https://github.com/deiry/FabricDefect https://github.com/ZCmeteor/Fabric-defect-recognition
自编码器异常检测交互可视化介绍 https://anomagram.fastforwardlabs.com/#/
https://github.com/yzhao062/Pyod
【改进机器学习模型分布外异常检测】 https://ai.googleblog.com/2019/12/improving-out-of-distribution-detection.html
【表面缺陷检测文献集】’surface-defect-detection' https://github.com/Eatzhy/surface-defect-detection
https://medium.com/pytorch/road-defect-detection-using-deep-active-learning-98d94fe854d
Rethinking Assumptions in Deep Anomaly Detection https://github.com/lukasruff/Classification-AD
【PyAnomaly：PyTorch异常检测工具包】 https://github.com/YuhaoCheng/PyAnomaly
【PyODDS：端到端异常检测包】’PyODDS - An End-to-end Outlier Detection System' https://github.com/datamllab/PyODDS
https://github.com/cizhenshi/TianchiGuangdong2019_2th
OD-test: A Less Biased Evaluation of Out-of-Distribution (Outlier) Detectors (PyTorch) https://github.com/ashafaei/OD-test
视频异常检测论文/代码集 https://github.com/fjchange/awesome-video-anomaly-detection
https://github.com/zillow/luminaire
Deep learning for time series forecasting：PyTorch深度学习时序预测、分类和异常检测库 https://github.com/AIStream-Peelout/flow-forecast
https://github.com/AdneneBoumessouer/MVTec-Anomaly-Detection
表面缺陷研究相关数据集与文献列表 https://github.com/Charmve/Surface-Defect-Detection
Learning Memory-guided Normality for Anomaly Detection https://github.com/cvlab-yonsei/MNAD
Official implementation of "Classification-Based Anomaly Detection for General Data" by Liron Bergman and Yedid Hoshen, ICLR 2020. https://github.com/lironber/GOAD
Source code of the KDD19 paper "Deep anomaly detection with deviation networks", weakly/partially supervised anomaly detection, few-shot anomaly detection https://github.com/GuansongPang/deviation-network
This is the official repository to the WACV 2021 paper "Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows" by Marco Rudolph, Bastian Wandt and Bodo Rosenhahn. https://github.com/marco-rudolph/differnet
Patch SVDD for Image anomaly detection. Paper: https://arxiv.org/abs/2006.16067 (published in ACCV 2020).
https://github.com/nuclearboy95/Anomaly-Detection-PatchSVDD-PyTorch
Sub-Image Anomaly Detection with Deep Pyramid Correspondences (SPADE) in PyTorch https://github.com/byungjae89/SPADE-pytorch
Repository for the paper "Rethinking Assumptions in Anomaly Detection" https://github.com/lukasruff/Classification-AD
Automating Outlier Detection via Meta-Learning (Code, API, and Contribution Instructions) https://github.com/yzhao062/MetaOD
This is an unofficial implementation of the paper “PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization”. https://github.com/xiahaifeng1995/PaDiM-Anomaly-Detection-Localization-master
Feature Space Singularity for Out-of-Distribution Detection. https://github.com/megvii-research/FSSD_OoD_Detection
We propose a theoretically motivated method, Adversarial Training with informative Outlier Mining (ATOM), which improves the robustness of OOD detection to various types of adversarial OOD inputs and establishes state-of-the-art performance. https://github.com/jfc43/informative-outlier-mining
Unsupervised Brain Anomaly Detection and Segmentation with Transformers https://arxiv.org/abs/2102.11650
https://github.com/AdneneBoumessouer/MVTec-Anomaly-Detection
PyTorch implementation of "Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection" https://github.com/byungjae89/MahalanobisAD-pytorch
Pixel-wise Anomaly Detection in Complex Driving Scenes https://www.arxiv-vanity.com/papers/2103.05445
https://github.com/Charmve/Surface-Defect-Detection
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization https://www.arxiv-vanity.com/papers/2104.04015
DeepLog：DeepLog异常检测模型的PyTorch实现 github.com/wuyifan18/DeepLog
PySAD：Python流数据异常检测库 github.com/selimfirat/pysad
《Driver Anomaly Detection: A Dataset and Contrastive Learning Approach》(2020) github.com/okankop/Driver-Anomaly-Detection
《PySAD: A Streaming Anomaly Detection Framework in Python》(2020) github.com/selimfirat/pysad
《Robust Subspace Recovery Layer for Unsupervised Anomaly Detection》(ICLR 2020) github.com/dmzou/RSRAE
Towards Total Recall in Industrial Anomaly Detection https://arxiv.org/abs/2106.08265
github.com/zurutech/anomaly-toolbox
anomalib：Python深度学习异常检测算法库，包括最先进的算法和功能，如实验管理、超参数优化和边缘推断 github.com/openvinotoolkit/anomalib https://arxiv.org/abs/2202.08341
tsod: 时序数据异常检测库 github.com/DHI/tsod
表面缺陷研究相关数据集与文献列表 github.com/Charmve/Surface-Defect-Detection
【最新异常检测算法实现大列表】’Implementation of SOTA Deep Anomaly Detection Methods' by GuansongPang GitHub: github.com/GuansongPang/SOTA-Deep-Anomaly-Detection
ADRepository: 现实世界异常检测数据集】'ADRepository: Real-world anomaly detection datasets' by GuansongPang GitHub: github.com/GuansongPang/ADRepository-Anomaly-detection-datasets
【DGLD：基于DGL的深度图异常检测库】'A Deep Graph Anomaly Detection Library based on DGL - Deep Graph Outlier Detection' by EagleLab-ZJU GitHub: github.com/EagleLab-ZJU/DGLD
【工业异常检测相关工作大列表】’Awesome Industrial Anomaly Detection - Awesome-Industrial-Anomaly-Detection' M3LAB GitHub: github.com/M-3LAB/awesome-industrial-anomaly-detection
【弱监督异常检测相关文献资源列表】’Weakly-supervised Anomaly Detection: A Survey - A Collection of Resources for Weakly-supervised Anomaly Detection (WSAD)' Yue Zhao GitHub: github.com/yzhao062/WSAD
提出一种零样本异常分割新框架SAA+，通过协同组装基础模型和混合提示正则化方法，实现了对多种异常模式的准确定位，并在多个基准测试数据集上取得了最先进的性能。 https://arxiv.org/abs/2305.10724 [CV]《Segment Any Anomaly without Training via Hybrid Prompt Regularization》Y Cao, X Xu, C Sun, Y Cheng, Z Du, L Gao, W Shen [Huazhong University of Science and Technology] (2023)
【Python Deep Outlier/Anomaly Detection (DeepOD)：基于深度学习的多变量/时间序列数据的异常检测框架，提供了多个模型和统一的实现】’Python Deep Outlier/Anomaly Detection (DeepOD) - Deep learning-based outlier/anomaly detection' Hongzuo Xu GitHub: github.com/xuhongzuo/DeepOD