Variational Variance: Simple and Reliable Predictive Variance Parameterization

8 Jun 2020Andrew StirnDavid A. Knowles

An often overlooked sleight of hand performed with variational autoencoders (VAEs), which has proliferated the literature, is to misrepresent the posterior predictive (decoder) distribution's expectation as a sample from that distribution. Jointly modeling the mean and variance for a normal predictive distribution can result in fragile optimization where the ultimately learned parameters can be ineffective at generating realistic samples... (read more)

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