SlimSAM is a novel compression method that significantly reduces the need for extensive training data while maintaining high performance levels. By utilizing an alternate slimming framework and disturbed Taylor pruning, SlimSAM achieves remarkable results with only 0.1% of the original SAM training data. The method effectively compresses the model by alternately pruning and distilling distinct sub-structures, addressing challenges related to limited data availability and complex model structures. SlimSAM outperforms existing compression methods in terms of parameter counts, MACs, and training data requirements.
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by Zigeng Chen,... о arxiv.org 03-19-2024
https://arxiv.org/pdf/2312.05284.pdfГлибші Запити