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Frontiers24 a
Three-Dimensional Convolutional Autoencoder Extracts Features of Structural Brain Images With a “Diagnostic Label-Free” Approach: Application to Schizophrenia ... - Frontiers
The decoder learns to reproduce the input as close as possible to the original using the latent representations. 3D-CAE extends this architecture by using convolutional layers that can extract ...
GitHub4 mois
RitiP/TensorFlow-Convolutional-AutoEncoder: This is an implementation of Convolutional AutoEncoder using only TensorFlow - GitHub
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 ...
GitHub4 a
AquibPy/Convolutional-Variational-Autoencoder: PyTorch implementation of Variational Autoencoder. - GitHub
Encoder — The encoder consists of two convolutional layers, followed by two separated fully-connected layer that both takes the convoluted feature map as input. The two full-connected layers output ...
IEEE5 a
Video Anomaly Detection using Convolutional Spatiotemporal Autoencoder - IEEE Xplore
A convolutional spatiotemporal autoencoder is used for video anomaly detection. The proposed model architecture comprises of three major sections, such as spatial encoder, temporal encoder-decoder, ...
IEEE1 a
Optimization of Graph Convolutional Networks with Variational Graph Autoencoder Architecture for 3D Face Reconstruction Task
The study addresses the fundamental challenges encountered in 3D face reconstruction, including the inadequacy of dedicated research initiatives, the complexity of hyperparameter optimization, the ...