Abstract: Due to the strong feature extraction capabilities, convolutional neural networks (CNNs ... The proposed processor is experimentally tested in which the MNIST and Fashion MNIST datasets are ...

Abstract: We propose Denoising Masked Autoencoder (Deno-MAE), a novel multimodal autoencoder framework for denoising modulation signals during pretraining. DenoMAE extends the concept of masked ...

This project implements a Variational Autoencoder (VAE) in PyTorch, trained on the MNIST dataset. It features a Gradio web app for generating new digits and reconstructing uploaded digit images.

An autoencoder will first encode the image into a lower-dimensional representation, then decodes the representation back to the image.The goal of an autoencoder is to get an output that is identical ...