Official PyTorch code for WACV 2022 paper "CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows"
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Updated
Aug 18, 2023 - Python
Official PyTorch code for WACV 2022 paper "CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows"
unoffical and work in progress PyTorch implementation of CutPaste
Repository for the Explainable Deep One-Class Classification paper
Vanilla torch and timm industrial knn-based anomaly detection for images.
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.
Code underlying our publication "Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection" at ICPR2020
This is an unofficial implementation of Reconstruction by inpainting for visual anomaly detection (RIAD).
Anomaly detection method that incorporates multi-scale features to sparse coding
Code to reproduce 'Combining GANs and AutoEncoders for efficient anomaly detection'
Semi-Orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation
🐬 Re-implementation of PaDiM and code for the article "Weakly Supervised Detection of Marine Animals in High Resolution Aerial Images"
🪥 Unofficial re-implementation of Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation
This is an unofficial implementation of ' Anomaly localization by modeling perceptual features'
EfficientNetV2 based PaDiM
PatchCore method for Industrial Anomaly Detection + CLIP
The solutions for the dacon competition (1st place).
Repository for the Exposing Outlier Exposure paper
Thesis project about Visual Anomaly Detection based on Self Supervised Learning. The model identifies anomalies from information acquired during training, where normality and anomaly patterns are built using syntetic data
Using SPADE for anomaly detection on MVTEC LOCO AD dataset
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