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 ...

utilising signals with probabilistic shaping designed with the aid of end-to-end learning of an autoencoder-based architecture. For the first time, this work reports bit mapping optimisation for ...

and then the autoencoder was trained to recover the original, nonperturbed signal.From an image processing standpoint, we can train an autoencoder to perform automatic image pre-processing for us. add ...

The hidden layer of the last autoencoder is input to SoftMax as the feature of the input data for classification. The algorithm mainly includes three steps: preprocessing the original EEG signals, ...

There was an error while loading. Please reload this page. This repository contains the implementation of a variational autoencoder (VAE) for generating synthetic EEG ...

The autoencoder tries to minimize reconstruction errors to generate ECG signals similar to input signals. The discriminator uses reconstructed and original data as the input, and is trained to ...

To create a set of training data, Peter inserted simulated signals into real data, and then used this dataset to train an AI algorithm called an autoencoder. As the autoencoder processed the data ...