  We introduce a version of a variational auto-encoder (VAE), which can
generate good perturbations of images, when trained on a complex dataset (in
our experiments, CIFAR-10). The net is using only two latent generative
dimensions per class, with uni-modal probability density. The price one has to
pay for good generation is that not all training images are well reconstructed.
An additional classifier is required to determine which training image is well
reconstructed and generally the weights of training images. Only training
images which are well reconstructed, can be perturbed. For good perturbations,
we use the tentative empirical drifts of well reconstructed images. The
construct is not predictive in the usual statistical sense.

Computer Vision and Pattern Recognition