  In this study, we propose the Affine Variational Autoencoder (AVAE), a
variant of Variational Autoencoder (VAE) designed to improve robustness by
overcoming the inability of VAEs to generalize to distributional shifts in the
form of affine perturbations. By optimizing an affine transform to maximize
ELBO, the proposed AVAE transforms an input to the training distribution
without the need to increase model complexity to model the full distribution of
affine transforms. In addition, we introduce a training procedure to create an
efficient model by learning a subset of the training distribution, and using
the AVAE to improve generalization and robustness to distributional shift at
test time. Experiments on affine perturbations demonstrate that the proposed
AVAE significantly improves generalization and robustness to distributional
shift in the form of affine perturbations without an increase in model
complexity.
