This project implements a convolutional ... digit dataset. The autoencoder learns to remove artificially added noise from digit images, reconstructing the original clean images. Conv2DTranspose layer ...

To address these limitations, we propose S2HGC, an end-to-end spectral-spatial hypergraph convolutional network designed for ... Specifically, a controllable autoencoder (CAE) is designed to reflect ...

This paper introduces ARGAE-MSCE, an Adversarial Regularized Graph Autoencoder (ARGAE) enhanced by a Multi-scale Chebyshev Convolutional (ChebConv ... Experimental validation on two gear multi-sensor ...

It uses layers (convolutional, pooling) to extract and learn spatial features from the images. The CNN in this project was built with the following architecture: Convolutional Layers: Extracts ...