CVAEs

A cVAE, or conditional Variational Autoencoder, is a type of generative model that extends the standard Variational Autoencoder (VAE) by incorporating conditional information into the generation process. While a standard VAE learns a latent representation of data and can generate new samples from that learned distribution, a cVAE allows the generation of samples that are conditioned on specific attributes or classes. This conditioning is achieved by feeding the conditional information, such as a class label or an image attribute, into both the encoder and the decoder networks of the VAE.

The encoder in a cVAE learns to map an input data point along with its corresponding condition to a lower-dimensional latent space, typically represented by a mean and variance of a Gaussian distribution. The decoder then takes a sample from this latent distribution and the same conditional information to reconstruct the original data point or generate a new one that conforms to the specified condition. This ability to control the output based on external factors makes cVAEs useful in various applications.

Applications of cVAEs include conditional image generation, where new images can be created based on a class label (e.g., generating images of cats or dogs). They are also employed in text generation, allowing the production of text with specific themes or styles. Furthermore, cVAEs can be used for data imputation, where missing values can be filled in based on the available context. The underlying principle is that by providing the desired condition, the model can guide the generation process towards a more specific and relevant output.