The surrogate loss of variational autoencoders (VAEs) poses various
challenges to their training, inducing the imbalance between task fitting and
representation inference. To avert this, the existing strategies for VAEs focus
on adjusting the tradeoff by introducing hyperparameters, deriving a tighter
bound under some mild assumptions, or decomposing the los