JDCOT : an Algorithm for Transfer Learning in Incomparable Domains using Optimal Transport
Résumé
Domain adaptation is a field of transfer learning where the training data (source) and the test data (target) come from different domains. The data in these two domains have therefore different underlying distributions, and the learned model should be adapted so that it can be used on the target data with good performance. We present here a domain adaptation algorithm to adapt heterogeneous domains, i.e. described by different features. The developed method uses optimal transport to map the distributions of the two domains and its implementation is illustrated on benchmark data.
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