English
资源说明：While humans easily recognize relations between data from different domains without any
supervision, learning to automatically discover
them is in general very challenging and needs
many ground-truth pairs that illustrate the relations. To avoid costly pairing, we address
the task of discovering cross-domain relations
given unpaired data. We propose a method based
on generative adversarial networks that learns
to discover relations between different domains
(DiscoGAN). Using the discovered relations, our
proposed network successfully transfers style
from one domain to another while preserving key
attributes such as orientation and face identity
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