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

Question

Can you name the methods that aid in data-efficient training of generative models with limited labeled data?

Answer 1

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.