Spotting errors in AI images requires noticing small details, but the human visual system isn’t wired for that when you’re ...

ABSTRACT: Industrial appearance anomaly detection (AD) focuses on accurately identifying and locating abnormal regions in images. However, due to issues such as scarce abnormal samples, complex ...

In the training stage, masked autoencoder is used as the generator to reconstruct the image, and a discriminator with the reconstructed image and the original image as input is added at the end ...

In modern manufacturing, image anomaly detection (IAD) is always performed at the end of the manufacturing process and tries to identify product defects. The price of a product is significantly af ...

To derive the final list of crossings, the start time of the first positive detection and the end time of the last ... features from the input image by training with the ground truth labels for each ...

Abstract: Hyperspectral anomaly target detection ... propose a novel end-to-end local invariant autoencoding density estimation (E2E-LIADE) model. To satisfy the assumption on the manifold, the ...

Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a dataset that are different from the majority for tasks like ...