This project implements a convolutional autoencoder for image denoising using the MNIST handwritten digit dataset. The autoencoder learns to remove artificially added noise from digit images, ...

To tackle this, this research introduced a feature weighting mechanism based on squeeze and excitation networks in a convolutional denoising autoencoder to act as channel-wise attention to reweigh ...

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 ...

A U-Net based denoising autoencoder was trained on this dataset and evaluated on three low-field datasets: the M4Raw dataset (0.3T), in vivo brain MRI (0.05T), and phantom images (0.05T).The NND ...

Geometric Dynamic Variational Autoencoders (GD-VAEs) for learning embedding maps for nonlinear dynamics into general latent spaces. This includes methods for standard latent spaces or manifold latent ...