[Variational Auto-Encoder(VAE)](https://arxiv.org/abs/1312.6114) is one of the generative model. From a neural network perspective, the only difference between the VAE and the Auto-Encoder(AE) is that the latent vector z in VAE is stochastically sampled. This solves the problem that the AE learns identity mapping and can not have meaningful representations in latent space. In fact, the VAE uses [reparameterization trick](https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/03-advanced/variational_auto_encoder/main.py#L40-L44) to enable back propagation without sampling z directly from the mean and variance.

Real image | Reconstruced image

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