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 ...

The model is trained until the loss is minimized and the data is reproduced as closely as possible. Through this process, an autoencoder can learn the important features of the data. While that’s a ...

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 ...

Behind the scenes, the autoencoder uses tanh() activation on the hidden nodes and tanh() activation on the output nodes. The result of the tanh() function is always between -1 and +1. Therefore, the ...