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PyTorch implementation of "Sub-Image Anomaly Detection with Deep Pyramid Correspondences"
An interactive lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The the lab content is based on Python, IPython Notebook, and PyTorch.
The main features of hcw-00/patchcore_anomaly_detection are: Anomaly Detection.
Open-source alternatives to hcw-00/patchcore_anomaly_detection include: byungjae89/mahalanobisad-pytorch. byungjae89/spade-pytorch — PyTorch implementation of "Sub-Image Anomaly Detection with Deep Pyramid Correspondences". cqylunlun/glass. gitihubi/deepai — An interactive lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of… xiahaifeng1995/padim-anomaly-detection-localization-master — This is an unofficial implementation of the paper “PaDiM: a Patch Distribution Modeling Framework for Anomaly…