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 ...

To address these challenges, we developed a deep-learning based pipeline designed to streamline the training and execution of 3D autoencoder models for particle picking in cryo-ET experiments. Our ...

data and a 3D convolutional autoencoder. The study uses a combination of five different vegetation indices: Normalized Difference Vegetation Index (NDVI), Red-Edge Chlorophyll Vegetation Index (RECl), ...

In this paper, after obtaining 3D features, we construct the convolutional autoencoder to distinguish the state of emotion by combining CNN and SAE. Figure 7 shows the overall network structure.

The purpose of this study was to investigate the efficacy of a 3D convolutional autoencoder (3D-CAE) for extracting features related to psychiatric disorders without diagnostic labels. The network was ...

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

Therefore, in this paper, an unsupervised spatial-spectral feature learning strategy is proposed for hyperspectral images using 3-Dimensional (3D) convolutional autoencoder (3D-CAE). The proposed ...