Unsupervised Cross-Domain Image Generation

7 Nov 2016Yaniv TaigmanAdam PolyakLior Wolf

We study the problem of transferring a sample in one domain to an analog sample in another domain. Given two related domains, S and T, we would like to learn a generative function G that maps an input sample from S to the domain T, such that the output of a given function f, which accepts inputs in either domains, would remain unchanged... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Unsupervised Image-To-Image Translation SVNH-to-MNIST DTN Classification Accuracy 84.4% # 2

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