Generative Adversarial Networks for Synthetic Data Generation

GANs consist of a generator and discriminator trained in a minimax game. The generator learns to produce samples from the real data distribution.

Key Uses:

Data augmentation for imbalanced medical datasets and privacy-preserving synthetic data.

Hacks:

Use Wasserstein GAN with Gradient Penalty (WGAN-GP) for training stability. Apply StyleGAN’s style-based generator for fine-grained control. Conditional GANs (cGANs) for class-specific generation. Mixup and CutMix techniques to further augment synthetic data quality.

References:

Good fellow, I., et al. (2014). Generative Adversarial Nets. NeurIPS.

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