In our at-tempt, we present a “learning via translation” framework.In the baseline, we translate the labeled images from sourceto target domain in an unsupervised manner.
we propose to preserve two types of unsupervised similari-ties, 1) self-similarity of an image before and after transla-tion, and 2) domain-dissimilarity of a translated source im-age and a target image. Both constraints are implementedin the similarity preserving generative adversarial network(SPGAN) which consists of an Siamese network and a Cy-cleGAN.
A brief summary of different methods considered in this paper ispresented in Table 1.
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