Variational Auto-Encoder(VAE) is a 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 to enable back propagation without sampling z directly from the mean and variance.
As in conventional auto-encoders, the VAE minimizes the reconstruction loss between the input image and the generated image. In addition, the VAE approximates z to the standard normal distribution so that the decoder in the VAE can be used for sampling in the test phase.
$ pip install -r requirements.txt
$ python main.py| Real image | Reconstruced image |
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