Shallow Domain Adaptation Methods Sample Clauses

Shallow Domain Adaptation Methods. The most preva- lent domain adaptation methods accomplish domain adapta- tion task by reducing the discrepancy between source and target distributions via computing a feature transformation. We chose CORrelation ALignment (CORAL) [7] and Subspace Alignment (SA) [8] which are two popular methods following this approach. Reported performances for both methods are appealing whereas their application to high dimensional data might be problematic since they are not scalable to high number of features. Another approach for domain adaptation is modeling the dependence of source and target domain in feature level. We experimented by a recent work, namely Feature Level Domain Adaptation (FLDA) [9], that follows this approach. We use published source codes of the shallow DA methods for all datasets.
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