VAE.ipynb: This notebook guides the user through the process of preparing the dataset, training the Variational Autoencoder model, and evaluating its performance. It includes steps for data loading, ...

A variational autoencoder produces a probability distribution for the different features of the training images/the latent attributes. When training, the encoder creates latent distributions for the ...

The dimension of the latent space is set to 2. The variational autoencoder with 4 hidden layers performed the best with high Spearman and Pearson coefficients and low RMSD. To further evaluate the ...

In order to run the autoencoder you should have as working directory the Autoencoder directory and run on the terminal the following command: is the path to the dataset that we will evaluate the ...

Abstract: A method for explaining a deep learning model prediction is proposed. It uses a combination of the standard autoencoder and the variational autoencoder. The standard autoencoder is exploited ...

Abstract: Autoencoder is an excellent unsupervised learning algorithm. However, it can not generate kinds of sample data in the decoding process. Variational autoencoder is a typical generative ...