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Mix and match networks: encoder-decoder alignment for zero-pair image translation
[article]
2018
arXiv
pre-print
We address the problem of image translation between domains or modalities for which no direct paired data is available (i.e. zero-pair translation). We propose mix and match networks, based on multiple encoders and decoders aligned in such a way that other encoder-decoder pairs can be composed at test time to perform unseen image translation tasks between domains or modalities for which explicit paired samples were not seen during training. We study the impact of autoencoders, side information
arXiv:1804.02199v1
fatcat:hj5wbxobsndqjmy4hkzz3g6l5m