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A variational autoencoder is a specific type of neural network that helps to generate complex models based on data sets. In general, autoencoders are often talked about as a type of deep learning network that tries to reconstruct a model or match the target outputs to provided inputs through the principle of backpropagation.
Variational autoencoders use probability modeling in a neural network system to provide the kinds of equilibrium that autoencoders are typically used to produce. The variational autoencoder works with an encoder, a decoder and a loss function. By reconstructing loss aspects, the system can learn to focus on desired likelihoods or outputs, for example, producing remarkable focus in image generation and image processing. For example, tests of these types of networks show their ability to reconstruct and render numerical digits from inputs.