  Image-to-image translation has played an important role in enabling synthetic
data for computer vision. However, if the source and target domains have a
large semantic mismatch, existing techniques often suffer from source content
corruption aka semantic flipping. To address this problem, we propose a new
paradigm for image-to-image translation using Vector Symbolic Architectures
(VSA), a theoretical framework which defines algebraic operations in a
high-dimensional vector (hypervector) space. We introduce VSA-based constraints
on adversarial learning for source-to-target translations by learning a
hypervector mapping that inverts the translation to ensure consistency with
source content. We show both qualitatively and quantitatively that our method
improves over other state-of-the-art techniques.
