what way does transfer learning specifically benefit smaller datasets hen training generative Al models?

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Iransfer leaming primarily increases the computational speed, making the size of the dataset irrelevant to the training process.

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It enables smaller datasets to be expanded artificially, creating more data points for training generative Al models.

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Transferleaming allows models pre$-$trainnd on large datasets to adapt to similar tasks with smalier datasets enhancing learning efficiency and model performance without the need for extensive data. $q,$ This method reduces the accuracy of the models to match the lower quality of smaller datasets. ensuring consistency in performance.