The experiment was carried out in two stages. In the first one, different activation functions, GLN, Tanh, and Sine, were tested in an MLP-type Autoencoder neural network model. Different compression ...

This repository presents a novel autoencoder algorithm designed using the Hartley Transform as a key component, coupled with involutional activation functions. The architecture incorporates various ...

Various methods have been proposed to address this problem such as AutoEncoder, Dropout, DropConnect, and Factored Mean training. In this paper, we propose a denoising autoencoder approach using a ...

It consists of four blocks which include a convolutional layer, a batch normalization layer, and ReLU activation function, followed by an upsampling layer. As a result of the convolutional and the ...

The MLPRegressor can function as an autoencoder by passing X as input and target (i.e. X == y). I use PCA for dimensionality reduction a lot, but kept going to torch for autoencoders for comparison ...