Hold
Why?
Access to diverse and extensive datasets is crucial for training robust AI models, but sourcing this data can be challenging. Generative AI offers a solution to:
- Enhance dataset diversity, improving model performance on varied inputs.
- Increase the volume of training data, critical for deep learning models.
- Generate synthetic data where real data is scarce or sensitive.
What?
Utilizing generative AI techniques to:
- Create realistic, synthetic datasets that closely mimic genuine data attributes.
- Augment existing datasets, filling gaps and extending dataset utility without compromising privacy.
- Facilitate more efficient and effective model training across industries.
References
- Confluence page "Data Augmentation With Generative AI" in AD space
- "Data augmentation using Generative AI" section in the presentation from 2023-08-23
- Presentation "Synthetically generated datasets"
- Google Folder "Improving TKits on small datasets"