What is VAE model?
We introduce now, in this post, the other major kind of deep generative models: Variational Autoencoders (VAEs). In a nutshell, a VAE is an autoencoder whose encodings distribution is regularised during the training in order to ensure that its latent space has good properties allowing us to generate some new data.
What is the purpose of the Reparameterize function in our VAE class?
Autoencoders are a class of generative models. They allow us to compress a large input feature space to a much smaller one which can later be reconstructed. Compression, in general, has got a lot of significance with the quality of learning.
What does Reparameterize mean?
reparameterizationnoun. A second or subsequent parameterization.
What does a variational autoencoder ( VAE ) do?
A variational autoencoder (VAE) is a type of neural network that learns to reproduce its input, and also map data to latent space. A VAE can generate samples by first sampling from the latent space. We will go into much more detail about what that actually means for the remainder of the article.
How is a VAE used to generate samples?
A VAE can generate samples by first sampling from the latent space. We will go into much more detail about what that actually means for the remainder of the article. As defined earlier, an autoencoder is just a neural network that learns to reproduce its input.
What are the two parts of a VAE?
As we know a VAE is a neural network that comes in two parts: the encoder and the decoder. These are split in the middle, which as discussed is typically smaller than the input size. We typically call the values at the hidden layer z and they represent the latent variable representation of the input data.
How are Variational autoencoders used in deep generative models?
We introduce now, in this post, the other major kind of deep generative models: Variational Autoencoders (VAEs). In a nutshell, a VAE is an autoencoder whose encodings distribution is regularised during the training in order to ensure that its latent space has good properties allowing us to generate some new data.