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Transferring learned models to novel tasks is a challenging problem, particularly if only very few labeled examples are available.
WordNet: a lexical database for English
George A Miller · 1995
Earlier work this paper cites.
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Sebastian Thrun · 1996
Earlier work this paper cites.
Learning from one example through shared densities on transforms
Erik G. Miller, Nicholas E Matsakis, and Paul A. Viola · 2000
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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