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Fundamental Limits of Transfer Learning in Binary Classifications
Transfer learning is gaining traction as a promising technique to alleviate this barrier by utilizing the data of a related but different \emph{source} task to compensate for the lack of data in a \emph{target} task where there are few labeled training data.
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