Contents
What makes a VAE a latent variable model?
VAE are latent variable models [1,2]. Such models rely on the idea that the data generated by a model can be parametrized by some variables that will generate some specific characteristics of a given data point. These variables are called latent variables.
How is the VAE a variational inference problem?
The VAE is a variational inference problem: We have an observed data that was created using latent variables and we wish to find the optimal distribution for generating these latent variables. At the inference stage we use this function to generate new data. What we saw is that this function cannot assumed to be a standard Normal distribution.
What’s the difference between a VAE and a sample?
The two primary differences – that samples are encoded as two vectors that represent a probability distribution over the latent space rather than a point in latent space and that Kullback-Leibler divergence is added to optimization – will be covered in more detail. Through these, we’ll see why VAEs are suitable for generating content.
How is the distribution of Z’s in VAE constructed?
As we know VAE is constructed of two networks: one ( the encoder) is trained to map real data into a Gaussian distribution aiming to optimize its KL distance from the a given distribution (typically standard Normal dist.) and the other ( the decoder) to map samples of this Gaussian distribution ( Z ’s) into a real data.
Are there any drawbacks to using Vaes?
However, VAEs also have some drawbacks: Generated images are blurry because the mean square error tend to make the generator converge to an averaged optimum. Generative Adverserial Networks (GANs) solve the latter issue by using a discriminator instead of a mean square error loss and produce much more realistic images.
How is variational inference ( VI ) used in statistics?
In statistics, variational inference (VI) is a technique to approximate complex distributions. The idea is to set a parametrised family of distribution (for example the family of Gaussians, whose parameters are the mean and the covariance) and to look for the best approximation of our target distribution among this family.
What makes a VAE a variational autoencoder?
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.