Convolutional autoencoders leverage convolutional ... The variational autoencoder marks a significant advancement in autoencoder architecture by embracing a probabilistic representation of the ...

We are using Spatio Temporal AutoEncoder and more importantly three models from Keras ie; Convolutional 3D, Convolutional 2D LSTM and Convolutional 3D Transpose.

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 ...

Convolutional Autoencoders are a type of neural network architecture that combines convolutional layers for feature extraction with transpose convolutional layers for upsampling, making them ...

The most basic architecture of an autoencoder is a feed-forward ... Similar to convolution neural networks, a convolutional autoencoder specializes in the learning of image data, and it uses a filter ...

This block can seamlessly be integrated into an existing autoencoder architecture to facilitate SU analysis. In order to perform the endmember extraction and the fractional abundance map estimation, a ...