Recent works on learned image coding using autoencoder models have achieved promising results in rate-distortion performance. Typically, an autoencoder is used to transform an image into a latent ...

In this paper, a branching convolutional encoder (BCE)-based spatio-spectral hyperspectral image dimensionality reduction model is presented. The architecture consists of a pointwise separable ...

FIGURE 3. One-dimensional convolutional block attention module. The specific main process of the feature channel attention module shows as follows: an input feature F ∈ ℝ T × C along the time ...

Similarly, each block of the decoder includes a 1D convolutional layer, a ReLu activation layer, and an upsampling layer. The reconstructed output vector is compared with the original input vector, ...

This methodology is referred to as a deep convolutional neural network with deconvolution and a deep autoencoder (DDD). By applying Grad-CAM in the constructed neural network, it is possible to detect ...

If you’ve read about unsupervised learning techniques before, you may have come across the term “autoencoder”. Autoencoders are one of the primary ways that unsupervised learning models are developed.

Keras autoencoders (convolutional/fcc) This is an implementation of weight-tieing layers that can be used to consturct convolutional autoencoder and simple fully connected autoencoder. It might feel ...