Autoencoders enable us to distil information by utilising a neural network architecture composed of an encoder and decoder. There are multiple types of autoencoders that vary based on their structure ...

It is composed of an encoder network and a decoder network, which work together to learn a compressed representation of input data. The purpose of an autoencoder is to learn a lower-dimensional ...

In this paper, we aim to find a suitable compression policy from DNNs’ structural information. We propose an automatic graph encoder-decoder model compression (AGMC) method combined with graph neural ...

This model is based on a fully convolutional auto-encoder and can be trained end-to-end. It consists of two parts: encoder and decoder. The encoder and decoder ... Second, to solve the problem of ...

In this work, we present a novel Encoder-Decoder Graph Convolutional Network (ED-GCN) to perform auto-segmentation on two widely accepted clinical tests for human mobility and balance assessment: the ...

The decoder will receive the data passing through the channel to recover the transmitted symbols through learning the neural network. The auto-encoder in the model replaces the coding and modulation ...