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 ...

Instead, the activations within a given layer are penalized, setting it up so the loss function better captures the statistical features of input data. To put that another way, while the hidden layers ...

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 ...

Many of the autoencoder examples I see online use relu() activation for interior layers. The relu() function was designed for use with very deep neural architectures. For autoencoders, which are ...

Robustness of the representation for the data is done by applying a penalty term to the loss function. Contractive autoencoder is another regularization technique just like sparse and denoising ...

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 ...

Besides, the loss function of the variational autoencoder is revised and improved. The aim is to learn feature representations with fewer image features to obtain more accurate results. (2) In the ...