An autoencoder is a unique form of deep learning architecture that comprises two main components ... Its purpose is to reduce the dimensionality of the input image by passing it through convolutional ...

And based on these results we made the changes to the model architecture. Here we evaluate the results of the autoencoder model. We picked the first 10 images from the test dataset for this test.

This paper proposes and implements a deep convolutional autoencoder architecture that maximizes the image colorization performance on two different datasets, the Fruit-360 and Flickr-Faces-HQ. To this ...

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 ...

Proposal of the approach is based on using neural networks with an auto encoder architecture, which allows us to reconstruct images with high accuracy. Experiments have shown that the mapped ...

The network reconstructs the input data in a much similar way by learning its representation. The basic architecture of am Autoencoder is shown below. (Image Source: Wikipedia) The architecture ...

Subsequently, we detail the architecture and training process of the proposed autoencoder model, and present the results of generating MRI images for ASD and non-ASD patients. Following this, we ...

Using the log of the variance helps prevent values from becoming excessively large. [Click on image for larger view.] Figure 2: Variational Autoencoder Architecture for the UCI Digits Dataset The key ...