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 ...

Auto Graph Encoder-Decoder for Neural Network Pruning Abstract: Model compression aims to deploy deep neural networks (DNN) on mobile devices with limited computing and storage resources. However, ...

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 ...

Convolutional auto-encoders have shown their remarkable performance in stacking deep convolutional neural networks for classifying image data during the past several years. However, they are unable to ...

Abstract: Vision-based action segmentation is an important tool in human movement analysis. In this work, we present a novel Encoder-Decoder Graph Convolutional Network (ED-GCN) to perform ...