An autoencoder is a type of artificial neural network commonly used to learn efficient representations of data, typically for dimensionality reduction, data compression, or denoising (noise removal).

This project implements a Convolutional AutoEncoder for image compression using the CIFAR-100 dataset. The autoencoder is designed to compress images into a lower-dimensional latent space and ...

Abstract: The end-to-end autoencoder is a novel and attracting concept to innovate communication system architecture. In its training stage, the end-to-end autoencoder needs differentiable channel ...

Abstract: Masked Autoencoder (MAE) has shown remarkable potential in self-supervised representation learning for 3D point clouds. However, these methods primarily rely on point-level or low-level ...

Traffic prediction is the core of intelligent transportation system, and accurate traffic speed prediction is the key to optimize traffic management. Currently, the traffic speed prediction model ...