This is an implementation of a quantum autoencoder for image denoising. The autoencoder is trained on a set of images with added noise. The autoencoder is then used to denoise the same set of images.

denoising-autoencoder-from-scratch Overview Autoencoders are a type of neural network used to learn efficient representations of data, typically for dimensionality reduction or feature learning.

The cost of an accurate medical diagnosis is extremely high, particularly in developing nations. Pneumonia is a prevalent ailment that poses a considerable barrier to medical diagnosis because of the ...

Electrocardiogram (ECG) signals are widely utilized for cardiovascular disease monitoring. However, these signals are often susceptible to various types of noise during acquisition, which can ...

The deep neural network architecture, called a denoising autoencoder, is similar to FlowNet and U-Net and consists of encoder and decoder components to progressively subsample and upsample inputs ...

Compared to using PCA for dimensionality reduction, using a neural autoencoder has the big advantage that it works with source data that contains both numeric and categorical data, while PCA works ...