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

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

Official Implementation for the "Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection" paper (VAND Workshop - CVPR 2023).

[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

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

Project: Unsupervised Anomaly Segmentation via Deep Feature Reconstruction

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"

This is an official implementation of “ Prototypical Learning Guided Context-Aware Segmentation Network for Few-Shot Anomaly Detection” (PCSNet) with PyTorch.

Semi-Orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation

This is a cross-modal benchmark for industrial anomaly detection.

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

The implementation of the paper Foundation Visual Encoders Are Secretly Few-Shot Anomaly Detectors

Unsupervised Anomaly Detection and Segmentation via Deep Feature Correspondence

[GCPR 2023] UGainS: Uncertainty Guided Anomaly Instance Segmentation

Learning Diffusion Models for Multi-View Anomaly Detection [ECCV2024]

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