LSTM autoencoder is an encoder that makes use of LSTM encoder-decoder architecture to compress data using an encoder and decode it to retain original structure using a decoder. by Ankit Das Simple ...

In this letter, we propose ConvAE, a new channel autoencoder structure. ConvAE uses residual blocks with convolutional layers. This configuration increases performance while decreasing computational ...

Stacked autoencoder is a typical deep neural network. The hidden layers will compress the input data with a better representation than the raw data. Stacked autoencoder has several hidden layers.

This is a simple example of using a neural network as an autoencoder without using any machine learning libraries in Python. The input is a 8-bit binary digits and as expected the output is the same 8 ...

ResNet-18 represents a specific configuration within the Residual Network (ResNet) architecture, featuring a total of 18 layers. Its core structure is built upon basic residual blocks, where each ...

The proposed autoencoder network design enabled the best possible original EDXRF spectrum reconstruction and the most informative feature extraction, which was used for dimension reduction.