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Brenier approach for optimal transportation between a quasi-discrete measure and a discrete measure
Correctly estimating the discrepancy between two data distributions has always been an important task in Machine Learning.
Optimal Transport for Deep Joint Transfer Learning
By adding an Optimal Transport loss (OT loss) between source and target classifier predictions as a constraint on the source classifier, the proposed Joint Transfer Learning Network (JTLN) can effectively learn useful knowledge for target classification from source data.
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