This project focuses on utilizing Convolutional Autoencoders (CAEs) to reconstruct MNIST dataset digits. Convolutional Autoencoders are a type of neural network architecture that combines ...

Encoder — The encoder consists of two convolutional layers, followed by two separated fully-connected layer that both takes the convoluted feature map as input. The two full-connected layers output ...

Abstract: In the remote sensing context spectral unmixing is a technique to decompose a mixed pixel into two fundamental representatives: endmembers and abundances. In this paper, a novel architecture ...

A convolutional spatiotemporal autoencoder is used for video anomaly detection. The proposed model architecture comprises of three major sections, such as spatial encoder, temporal encoder-decoder, ...

In this paper, the stacked convolutional autoencoder network structure constructed with fusion selection kernel attention mechanism is based on FCAE, which consists of an encoder and a decoder. It can ...

For the specific structure of the convolutional autoencoder part, see Section 2.1. The last part is the discriminator. The three discriminators D b + , D b and D m have a similar structure, which ...