The deep neural network architecture, called a denoising autoencoder, is similar to FlowNet and U-Net and consists of encoder and decoder components to progressively subsample and upsample inputs ...

Then we train the denoising autoencoder with realizations and templates of the point spread function. After training, the denoising autoencoder learns the manifold space of the point spread function ...

One of the most critical parts of energy management is energy monitoring. As a result, it is necessary to monitor a facility's power consumption before implementing technical measures to reduce it.

Scanning transmission electron microscopy explained By comparing these benchmarked data with their experimental results, the researchers demonstrated a greatly enhanced performance when using their ...

Compared to using PCA for dimensionality reduction, using a neural autoencoder has the big advantage that it works with source data that contains both numeric and categorical data, while PCA works ...