A generic sparse autoencoder is visualized where the obscurity of a node corresponds with the level of activation. Sparsity constraint is introduced on the hidden layer.

Convolutional Autoencoder: The core of the project is a convolutional autoencoder architecture, which learns to encode and decode image features to perform effective colorization. Grayscale to Color: ...

In this article, we will define a Convolutional Autoencoder in PyTorch and train it on the CIFAR-10 dataset in the CUDA environment to create reconstructed images.

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 ...

The analysis of deforming 3D surface meshes is accelerated by autoencoders since the low-dimensional embeddings can be used to visualize underlying dynamics. But, state-of-the-art mesh convolutional ...

Thus, we propose an ECG anomaly detection framework (ECG-AAE) based on an adversarial autoencoder and temporal convolutional network (TCN) which consists of three modules (autoencoder, discriminator, ...