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This paper studies the problem of learning the conditional distribution of a high-dimensional output given an input, where the output and input may belong to two different domains, e.g., the output is a photo image and the input is a sketch image.
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J. Xie, Z. Zheng, X. Fang, S.-C. Zhu, and Y. N. Wu, “Learning cycle-consistent cooperative networks via alternating MCMC teaching for unsupervised cross-domain translation,” in The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI) , 2021
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