Domain Transformer: Predicting Samples of Unseen, Future Domains

TL;DR

This paper introduces a domain transformer that predicts and generates data for unseen domains by learning transformations between known domains, enabling proactive adaptation to evolving data distributions.

Contribution

The paper proposes an unsupervised domain transformer using Cycle-GAN to generate data for unseen domains, advancing beyond reactive adaptation methods.

Findings

Effective in predicting unseen domain data

Improves unsupervised domain adaptation performance

Validated on CNN image classification tasks

Abstract

The data distribution commonly evolves over time leading to problems such as concept drift that often decrease classifier performance. Current techniques are not adequate for this problem because they either require detailed knowledge of the transformation or are not suited for anticipating unseen domains but can only adapt to domains, where data samples are available. We seek to predict unseen data (and their labels) allowing us to tackle challenges s a non-constant data distribution in a 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 of 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 only labels but no samples have to be predicted. Code is available at https://github.com/JohnTailor/DoTra.