The dataset used consists of about 12600 images. Contrast Limited Adaptive Histogram Equalization is applied to all images before feeding them as input to the trained transforming auto-encoder.

In this paper, we propose a novel convolutional encoder-decoder network with skip connections, named CEDNS, to improve the performance of saliency prediction. The encoder network utilizes the DenseNet ...

Autoencoders enable us to distil information by utilising a neural network architecture composed of an encoder and decoder. There are multiple types of autoencoders that vary based on their structure ...

Python 3.7.9 PyTorch 1.6.0 (cuda 10.2) torchvision 0.7.0 torch-geometric 1.6.1 To evaluate the AGMC on the ILSVRC-2012 dataset, you need to first download the dataset from ImageNet and export the data ...

The decoder will receive the data passing through the channel to recover the transmitted symbols through learning the neural network. The auto-encoder in the model replaces the coding and modulation ...

Convolutional auto-encoders have shown their remarkable performance in stacking deep convolutional neural networks for classifying image data during the past several years. However, they are unable to ...