The Challenge
- Traditional augmentation techniques like rotation, scaling, translation, is not applicable for all kind of data.
- Small dataset size hinders the dl developments.
- Collecting additional data can be prohibitively expensive, especially for specialized domains
The Approach
- cGANs for Data Augmentation:
- Utilized conditional Generative Adversarial Networks (cGANs) to generate synthetic physiological data.
- cGANs learn the underlying data distribution and create diverse and realistic data samples.
Result & Added Value
- Data Expansion:
- Expanded the original small dataset by generating a substantial amount of new, realistic physiological data.
- Improved Model Performance:
- Augmented dataset enhanced model training, leading to improved predictive accuracy and generalization.
- Cost-Effective Solution:
- Reduced the cost and time associated with manual data collection while achieving comparable or superior results.
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