An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

Paper list and datasets for industrial image anomaly/defect detection (updating). 工业异常/瑕疵检测论文及数据集检索库(持续更新)。

Unofficial implementation of EfficientAD https://arxiv.org/abs/2303.14535

This project proposes an end-to-end framework for semi-supervised Anomaly Detection and Segmentation in images based on Deep Learning.

[NeurIPS 2022 Spotlight] GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models

[ICCV'23] Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation

Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

Official Implementation for the "Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection" paper.

Project: Unsupervised Anomaly Segmentation via Deep Feature Reconstruction

This is an unofficial implementation of Reconstruction by inpainting for visual anomaly detection (RIAD).

[AAAI-2024] Offical code for <Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt>.

Official code for 'Deep One-Class Classification via Interpolated Gaussian Descriptor' [AAAI 2022 Oral]

Implementation of our paper "Optimizing PatchCore for Few/many-shot Anomaly Detection"

Semi-Orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation

This repository contains code from our comparative study on state of the art unsupervised pathology detection and segmentation methods.

Transformer-based Models for Unsupervised Anomaly Segmentation in Brain MR Images

Unsupervised Anomaly Detection and Segmentation via Deep Feature Correspondence

[GCPR 2023] UGainS: Uncertainty Guided Anomaly Instance Segmentation

Official implementation of the paper "Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI" accepted to the MICCAI 2021 BrainLes workshop

Adversarially Training of Autoencoders for Unsupervised Anomaly Segmentation