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Dual Learning: Theoretical Study and an Algorithmic Extension
[article]
2020
arXiv
pre-print
Dual learning has been successfully applied in many machine learning applications including machine translation, image-to-image transformation, etc. The high-level idea of dual learning is very intuitive: if we map an x from one domain to another and then map it back, we should recover the original x. Although its effectiveness has been empirically verified, theoretical understanding of dual learning is still very limited. In this paper, we aim at understanding why and when dual learning works.
arXiv:2005.08238v1
fatcat:rhi5q6cosvg2nglqkeyt64r7li