How do you implement data augmentation for training generative models and can you share some code examples

Question

How can i implement data augmentation techniques for training a generative model? I am stuck trying to expand my dataset - Could you share some code examples or pointers to get started?

Answer 1

Implementing data augmentation during the training of generative models can help increase the dataset and improve model robustness. Here are some good techniques along with code examples to get you started.

Data Augmentation Techniques

Text Augmentation:

• Synonym Replacement: Replacing words with their synonyms
• Random Insertion: Introducing random words in text
• Back Translation: Translate a text into another language and translate it back to introduce different variations

Noise Injection: Introduce random noise by introducing typographical errors or changing punctuation.

• Sentence Shuffling: Shuffle sentences in a paragraph to generate new variations.

Code Examples
Here are some simple implementations of these techniques using Python:

1. Synonym Replacement

2. Back Translation

3. Sentence Shuffling