  Variational inference is a powerful tool for approximate inference. However,
it mainly focuses on the evidence lower bound as variational objective and the
development of other measures for variational inference is a promising area of
research. This paper proposes a robust modification of evidence and a lower
bound for the evidence, which is applicable when the majority of the training
set samples are random noise objects. We provide experiments for variational
autoencoders to show advantage of the objective over the evidence lower bound
on synthetic datasets obtained by adding uninformative noise objects to MNIST
and OMNIGLOT. Additionally, for the original MNIST and OMNIGLOT datasets we
observe a small improvement over the non-robust evidence lower bound.
