How do you implement a conditional VAE and what are the benefits of adding labels to the input and output?
Variational autoencoders (VAEs) are powerful generative models that can learn to reconstruct and sample complex data distributions, such as images, text, or audio. However, sometimes you may want to control the generation process by specifying some desired attributes or conditions, such as the class, style, or content of the output. This is where conditional VAEs (CVAEs) come in handy. In this article, you will learn how to implement a CVAE and what are the benefits of adding labels to the input and output.
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