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 ...

Daniel Cremers "Clustering with Deep Learning: Taxonomy and new methods" --pretrain EPOCHS Pretrain the autoencoder for specified #epochs specified by architecture on specified dataset --cluster ...

A Deep Learning architecture-based Long-Short Term Memory (LSTM) autoencoder algorithm is designed to recognize intrusive events from the central network gateways of AVs. The proposed IDS is evaluated ...

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

The most basic architecture of an autoencoder is a feed-forward architecture ... and fewer nodes compress the data more. In a deep autoencoder, while the number of layers can be any number that the ...

For the transceiver algorithm architecture, we have chosen an appropriate ... By introducing the dimension reduction layer in the autoencoder structure based on deep learning, we can extend the ...