Domain Transformer: Predicting Samples of Unseen, Future Domains
- URL: http://arxiv.org/abs/2106.06057v1
- Date: Thu, 10 Jun 2021 21:20:00 GMT
- Title: Domain Transformer: Predicting Samples of Unseen, Future Domains
- Authors: Johannes Schneider
- Abstract summary: 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.
- Score: 1.7310589008573272
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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