News

IEEE2y
A Deep Convolutional Autoencoder Architecture for Automatic Image Colorization
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 ...
GitHub3mon
Clustering audio files with classic algorithms and autoencoder
In addition, a deep convolutional autoencoder architecture for clustering data was implemented. The paper describes in detail the processing and preparation of the audio files, which included the ...
GitHub3y
Convolutional-VAE-for-3D-Turbulence-Data
In this project we implement a Convolutional Variational Autoencoder ... you can use the scripts and adapt them to your own 3D data. The diagram below shows the implemented CVAE architecture. In this ...
Frontiers28d
Integrating multi-source monitoring data and deep convolutional autoencoder technology for slope failure pattern recognition
The architecture of the model is shown in Figure 5. Figure 5. Model architecture diagram of the deep convolutional autoencoder. The input to the model is a 9 × 24 matrix, where 9 represents the 9 ...
Frontiers3y
Three-Dimensional Convolutional Autoencoder Extracts Features of Structural Brain Images With a “Diagnostic Label-Free” Approach: Application to Schizophrenia Datasets
The purpose of this study was to investigate the efficacy of a 3D convolutional autoencoder (3D-CAE ... particularly when investigating psychiatric disorders, the appropriate architecture of 3D-CAE ...