The core of Autoencoder is the code-decode operation. Both the encoder and decoder may be Convolutional Neural Network or fully ... Latent Space, which is the layers in the middle contains the decoded ...

convolutional_autoencoder.py shows an example of a CAE for the MNIST dataset. The structure of this conv autoencoder is shown below: The encoding part has 2 convolution layers (each followed by a ...

The proposed architecture, named Autoencoder-Aided GCN (AA-GCN), compresses the convolutional features in an information-rich embedding at multiple hidden layers, exploiting the presence of ...

Similar to convolution neural networks, a convolutional autoencoder specializes in the learning of image data, and it uses a filter that is moved across the entire image section by section. The ...

Abstract: In this letter, we propose ConvAE, a new channel autoencoder structure. ConvAE uses residual blocks with convolutional layers. This configuration increases performance while decreasing ...

(3) By comparing the multi-layer perceptron of one-dimensional input, the convolutional neural network of 3D input and the convolutional autoencoder of 3D input presented in this paper, it is found ...

convolutional autoencoder; COBRE, Center for Biomedical Research Excellence; CPZE, dose of antipsychotic medication; MRI, magnetic resonance imaging; SPM, Statistical Parametric Mapping. The ...