This repository demonstrates a simple autoencoder model for compressing and reconstructing images. The autoencoder is built using TensorFlow and Keras and is trained on a dataset of images to learn a ...

Autoencoders are primitive network models which generates the input in output layer, simply repeats the input. This function can decrease the noise in data or complete a missing part. Autoencoders ...

For instance, you could train an autoencoder on grainy images and then use the trained model to remove the grain/noise from the image. Let’s take a look at the architecture of an autoencoder. We’ll ...

Each image is 28 by 28 = 784 pixels ... 1.0] to match the input values. Many of the autoencoder examples I see online use relu() activation for interior layers. The relu() function was designed for ...

In many examples, we can find that the autoencoder has worked well with the field of computer vision. Especially, where the image space is continuous but these autoencoders are not so successful in ...

Abstract: In this paper, we treat the image generation task using an autoencoder, a representative latent model. Unlike many studies regularizing the latent variable’s distribution by assuming a ...

This study aims to explore an autoencoder-based method for generating brain MRI images of patients with Autism Spectrum Disorder (ASD) and non-ASD individuals, and to discriminate ASD based on the ...