9781836207139 by Miguel Gonzalez

Author:Miguel Gonzalez [Gonzalez, Miguel]
Language: eng
Format: epub
Published: 0101-01-01T00:00:00+00:00

5.3.1 Forward Pass

In order to generate the latent space distribution, the input data is passed through the encoder of the VAE, which consists of a series of layers that transform the data into a lower-dimensional representation. This representation is then used to compute the mean and log variance of the latent space distribution. The log variance is then converted into standard deviation so that the VAE can sample from the distribution and generate new data points.

It is important to note that the transformation of the data into a lower-dimensional representation is a crucial part of the VAE architecture. This is because the lower-dimensional representation captures the most important features of the data while discarding irrelevant details. This allows the VAE to generate new data points that are similar to the original data, but with some degree of variation.

The forward pass is just the first step in the VAE training process. Once the latent space distribution is generated, the next step is to sample from the distribution to generate new data points. This is done using the reparameterization trick, which allows the VAE to backpropagate through the sampling process and learn the optimal values for the encoder and decoder parameters.

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