By Jim Shimabukuro (assisted by Grok)
Editor
[Related: Nov 2025, Oct 2025, Aug 2025]
1. Synthetic Data Generation: Fueling AI Without Real-World Limits
Synthetic Data Generation creates artificial datasets mimicking real ones using techniques like GANs (Generative Adversarial Networks), diffusion models, and variational autoencoders to augment training without privacy risks. It generates diverse scenarios, balancing classes in imbalanced data, and simulates rare events, improving model robustness.
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