Network structure of the convolution autoencoder. As shown in Figure 8 ... is 4 × 4 × 128 and the number of convolution kernels is set to 256. To fuse the feature maps of the different channels and ...

whereas the Convoltuional Autoencoder has 3 layers in decoder with an upsampling layer, and latent feature map of size 28,56,40. conda create -n autoencoder python=3.8 #(optional) pip install ...

correlations between input features), this structure can be learned and consequently leveraged when forcing the input through the network's bottleneck. When we regularize an autoencoder so ... takes ...

In this letter, a method of EEG signal compression based on 2-D rhythm feature maps is proposed. Through discrete wavelet ... using encoding and reconstruction channels based on an autoencoder network ...

In this paper, we propose a dilated convolution network with edge fusion block and directional feature maps for cardiac MRI segmentation. The network uses feature fusion module to preserve boundary ...