What methods aid in data-efficient training of generative models with limited labeled data

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Can you name the methods that aid in data-efficient training of generative models with limited labeled data?
Nov 21 in Generative AI by Ashutosh
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1 answer to this question.

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In order to train generative models efficiently with limited labeled data, you can refer to the following strategies :

  • Transfer Learning: You can fine-tune pre-trained models on your data.
  • Data Augmentation: It is used to generate synthetic data variations to expand your dataset.
  • Semi-Supervised Learning: It leverages unlabeled data with techniques like self-training.
  • Few-Shot Learning: It Uses prompt-based methods or adapters for small datasets.

Here is the code snippet you can refer to:

The above code leverages transfer learning for data efficiency. By referring to the above, you can aid in data-efficient training of generative models with limited labeled data.

answered Nov 21 by harsh raj

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