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We address the problem of image translation between domains or modalities forwhich no direct paired data is available (i.e. zero-pair translation). Wepropose mix and match networks, based on multiple encoders and decoders alignedin such a way that other encoder-decoder pairs can be composed at test time toperform unseen image translation tasks between domains or modalities for whichexplicit paired samples were not seen during training. We study the impact ofautoencoders, side information and losses in improving the alignment andtransferability of trained pairwise translation models to unseen translations.We show our approach is scalable and can perform colorization and styletransfer between unseen combinations of domains. We evaluate our system in achallenging cross-modal setting where semantic segmentation is estimated fromdepth images, without explicit access to any depth-semantic segmentationtraining pairs. Our model outperforms baselines based on pix2pix and CycleGANmodels.

Mix and match networks: encoder-decoder alignment for zero-pair image translation