This study explores a convolutional autoencoder for image denoising with a proposed compositional subspace method. This modeling approach presents a structural and rigorous mathematical abstraction to ...

We designed a graph-informed convolutional autoencoder called GICA to extract high-level features from the functional connectivity features. Furthermore, an attention layer based on recurrence rate ...

In this paper, the stacked convolutional autoencoder network structure constructed with fusion selection kernel attention mechanism is based on FCAE, which consists of an encoder and a decoder.

2.1.2 Pan-sharpening convolutional autoencoder method To develop the proposed PS method, the CAE network was programmed to enhance the spatial details of the intensity (I) component of the resampled ...

PyTorch implementations of an Undercomplete Autoencoder and a Denoising Autoencoder that learns a lower dimensional latent space representation of images from the MNIST dataset.

The convolutional autoencoder (CAE) was proposed on convolutional neural network (CNN) and denoising autoencoder (DAE). CAE can address the corrupted input samples and high dimensional problem.

linux deep-learning cpp pytorch dcgan yolo autoencoder vae dimensionality-reduction object-detection convolutional-autoencoder pix2pix semantic-segmentation multiclass-classification anomaly-detection ...