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 ...

I trained this "architecture" on selfies (256*256 RGB) and the encoded representation is 4% the size of the original image and terminated the training procedure after only one epoch ... command to ...

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 ...

Strengthen security, reduce risk, and streamline compliance by incorporating AI protection and adaptability into your Zero Trust approach. With decades of enterprise experience and a vast set of ...

To begin to address this challenge, several studies have explored the use of supervised deep learning techniques using convolutional neural networks (CNNs) or transformers for improving cell ...

Additionally, we propose two different ... The CNN architecture is utilized for the task of text classification. In particular, the text undergoes preliminary word segmentation, followed by passing ...

Based on the deep learning method, the convolutional ... 2) The binary V-I trajectory feature maps the initial V-I trajectory to a matrix with a certain size, which can almost reflect the features of ...