The experimental results show that this method can effectively integrate the channel attention module and the fully convolutional autoencoder. Although it is an unsupervised feature learning model, it ...

Subsequently, a convolutional denoising autoencoder is constructed and optimized ... Denoised signal sample pairs are then fed into a 2D CNN-based siamese network to generate embedding vectors. Fault ...

Combining all improvements achieved the best results. These results indicate that the introduced 2D convolutional autoencoder and multi-sequence, multi-scale asynchronous information extraction ...

a 2D convolutional operation is performed to reconstruct the HSIs with filters of size (7 × 7 × 198); the weights of the last layer are the extracted endmembers for each image. The NSAE-SU model is an ...

The computer vision domain has seen significant advancement in the last decade, and this advancement can be mainly attributed to the emergence of convolutional neural networks (CNNs). CNNs’ impeccable ...

We are using Spatio Temporal AutoEncoder and more importantly three models from Keras ie; Convolutional 3D, Convolutional 2D LSTM and Convolutional 3D Transpose.

Convolutional Autoencoder is a variant of Convolutional Neural Networks ... feature extractors differently from general autoencoders that completely ignore the 2D image structure. In autoencoders, the ...

A convolutional variational autoencoder has been used to help experts to visually select the best training data set in order to improve the performances of the PD source classifier.