  We study the problem of mapping between a domain $A$, in which there is a
single training sample and a domain $B$, for which we have a richer training
set. The method we present is able to perform this mapping in both directions.
For example, we can transfer all MNIST images to the visual domain captured by
a single SVHN image and transform the SVHN image to the domain of the MNIST
images. Our method is based on employing one encoder and one decoder for each
domain, without utilizing weight sharing. The autoencoder of the single sample
domain is trained to match both this sample and the latent space of domain $B$.
Our results demonstrate convincing mapping between domains, where either the
source or the target domain are defined by a single sample, far surpassing
existing solutions. Our code is made publicly available at
https://github.com/tomercohen11/BiOST
