Due to the complexity of samples and the limitations in spatial resolution, the spectra in hyperspectral imaging (HSI) are generally contributed to by multiple components, making univariate analysis ...

This PCA shape model is then incorporated as a decoder together with a ResNet CNN and is trained end-to-end for instance and semantic segmentation. Experiments on two popular datasets reveal that the ...

The accurate monitoring of abnormal production conditions in cement process is the basis of intelligent control, which is of great significance to improve the intelligent level of cement production.

The Data Science Lab Autoencoder Anomaly Detection Using PyTorch Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a ...

The Data Science Lab Autoencoder Anomaly Detection Using PyTorch Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a ...

If you’ve read about unsupervised learning techniques before, you may have come across the term “autoencoder”. Autoencoders are one of the primary ways that unsupervised learning models are developed.

Drawing inspiration from principal component analysis and autoencoder, we propose the Principal Component Analysis Autoencoder (PCAAE). This is a novel autoencoder whose latent space verifies two ...