Web: http://arxiv.org/abs/2109.01903

June 23, 2022, 1:11 a.m. | Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo-Lopes, Hannaneh Hajishirzi, Ali Farhadi,

cs.LG updates on arXiv.org arxiv.org

Large pre-trained models such as CLIP or ALIGN offer consistent accuracy
across a range of data distributions when performing zero-shot inference (i.e.,
without fine-tuning on a specific dataset). Although existing fine-tuning
methods substantially improve accuracy on a given target distribution, they
often reduce robustness to distribution shifts. We address this tension by
introducing a simple and effective method for improving robustness while
fine-tuning: ensembling the weights of the zero-shot and fine-tuned models
(WiSE-FT). Compared to standard fine-tuning, WiSE-FT provides large …

arxiv cv fine fine-tuning models

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