convolutional_autoencoder.py shows an example of a CAE for the MNIST ... But it is actually easy to do so using TensorFlow's tf.nn.conv2d_transpose() method. The idea was to replace each entry in the ...

In our last article, we demonstrated the implementation of Deep Autoencoder in image reconstruction. In this article, we will define a Convolutional Autoencoder in PyTorch and train it on the CIFAR-10 ...

The selected significant features were used to train a novel deep-learning classifiers. We designed a graph-informed convolutional autoencoder called GICA to extract high-level features from the ...

In order to conquer this problem, a lightweight framework named “PDSE-Lite” based on Convolutional Autoencoder (CAE ... the output features maps of pretrained bottleneck, Conv2D #5, and Conv2D #6 ...

Abstract: In this paper we propose a convolutional autoencoder to address the problem of motion infilling for 3D human motion data. Given a start and end sequence, motion infilling aims to complete ...

But, state-of-the-art mesh convolutional autoencoders require a fixed connectivity of all input meshes handled by the autoencoder. This is due to either the use of spectral convolutional layers or ...

The Autoencoders, a variant of the artificial neural networks, are applied in the image process especially to reconstruct the images. The image reconstruction aims at generating a new set of images ...