Domain Transformer: Predicting Samples of Unseen, Future Domains

The data distribution commonly evolves over time leading to problems such as concept drift that often decrease classifier performance. We seek to predict unseen data (and their labels) allowing us to tackle challenges due to a non-constant data distribution in a \emph{proactive} manner rather than detecting and reacting to already existing changes that might already have led to errors. To this end, we learn a domain transformer in an unsupervised manner that allows generating data of unseen domains. Our approach first matches independently learned latent representations of two given domains obtained from an auto-encoder using a Cycle-GAN. In turn, a transformation of the original samples can be learned that can be applied iteratively to extrapolate to unseen domains. Our evaluation on CNNs on image data confirms the usefulness of the approach. It also achieves very good results on the well-known problem of unsupervised domain adaption, where labels but not samples have to be predicted.