Anomaly detection method that incorporates multi-scale features to sparse coding

Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

This is an unofficial implementation of ' Anomaly localization by modeling perceptual features'

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

Source code for my master thesis "Anomaly detection with Convolutional Autoencoder"

Semi-Orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation

Data loader for the MVTec dataset, a comprehensive real-world dataset for Unsupervised Anomaly Detection

Code to reproduce 'Combining GANs and AutoEncoders for efficient anomaly detection'

🪥 Unofficial re-implementation of Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation

The solutions for the dacon competition (1st place).

Keras implementation of AnoGAN model.

Using SPADE for anomaly detection on MVTEC LOCO AD dataset

Code underlying our publication "Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection" at ICPR2020

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

EfficientNetV2 based PaDiM

🐬 Re-implementation of PaDiM and code for the article "Weakly Supervised Detection of Marine Animals in High Resolution Aerial Images"

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

Reorganize KolektorSDD dataset as MVTecAD dataset's format. Report SOTA anomaly detection models in KolektorSDD.

Official PyTorch code for WACV 2022 paper "CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows"