Flexible Transfer Learning under Support and Model Shift

NeurIPS 2014 Xuezhi WangJeff Schneider

Transfer learning algorithms are used when one has sufficient training data for one supervised learning task (the source/training domain) but only very limited training data for a second task (the target/test domain) that is similar but not identical to the first. Previous work on transfer learning has focused on relatively restricted settings, where specific parts of the model are considered to be carried over between tasks... (read more)

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