Application of Neural Networks for Data Generation

In this work, we address neural networks as a generative model. We analyze neural networks in general and focus on the use of neural networks to generate data similar to already existing data. Based on the analysis, we decided to generate handwritten digits using an autoencoder and a generative-adversarial network.
The basis is to teach the autoencoder to display data from the MNIST dataset. Subsequently, we generate data that should represent data similar to those from the MNIST dataset, but in a reduced dimensionality. This data should be similar to data we get from the encoder part, which is the essence of this work. The decoder part will then be used to visually display digits from generated data.