What is a variational autoencoder?
A type of autoencoder that leverages the discrepancy between inputs and outputs to generate modified versions of the inputs.
variational autoencoder explained in plain English
A type of autoencoder that leverages the discrepancy between inputs and outputs to generate modified versions of the inputs. Variational autoencoders are useful for generative AI. VAEs are based on variational inference: a technique for estimating the parameters of a probability model.
Example
Practitioners refer to variational autoencoder when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
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