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 ...

Ranasinghe et al. (2020) proposed a convolutional autoencoder, where the encoder performs three convolutional operations, flatten and dense operations; the last dense layer is set to equal the number ...

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 ...

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.

This time we want you to build a deep convolutional autoencoder by... stacking more layers. Encoder The encoder part is pretty standard, we stack convolutional and pooling layers and finish with a ...