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title
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SMAT
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2026-01-11 01:12:00
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SMAT Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts Shengzhuang Chen 1 , Jihoon Tack 2 , Yunqiao Yang 1 , Yee Whye Teh 3 , Jonathan Richard Schwarz 4 °, Ying Wei 5 ° 1 CityU Hong Kong 2 KAIST 3 University of Oxford 4 Harvard University 5 NTU °Joint senior authorship, equal contribution [ arXiv ] [ Code ] Abstract Conventional wisdom suggests parameter efficient fine-tuning of foundation models as the state-of-the-art method for transfer learning in vision, replacing the rich literature of alternatives such as meta-learning. In trying to harness the best of both worlds, meta-tuning introduces a subsequent optimization stage of foundation models but has so far only shown limited success and crucially tends to underperform on out-of-distribution (OOD) tasks. In this pap...
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