How do you handle imbalanced datasets when training or fine-tuning generative models especially with class distribution biases

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Can you tell me how can i handle imbalanced datasets when training or fine-tuning generative models use python programming to show?
Nov 8 in Generative AI by Ashutosh
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1 answer to this question.

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You can easily handle imbalanced datasets when training or fine-tuning generative models by referring to following code:

In the above code techniques like Class Weights and SMOTE were implemented to modify loss to handle imbalance and oversampling the minority class to balance dataset.

answered Nov 8 by mehek

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