Generative Adversarial Network (GAN)

A generative adversarial network (GAN) trains a generator to produce synthetic data and a discriminator to distinguish synthetic from real data, so the generator learns to mimic the real data distribution.

생성적 적대 신경망(GAN)은 합성 데이터를 만드는 생성자와 합성 데이터와 실제 데이터를 구별하는 판별자를 함께 학습시켜, 생성자가 실제 데이터 분포를 모방하도록 한다.

How do I generate realistic synthetic data that mimics my real dataset?

실제 데이터와 비슷한 현실적인 합성 데이터를 어떻게 만드나요?

A generative adversarial network learns to produce synthetic records by training a generator against a discriminator that tries to tell real from fake data. It fits data augmentation and synthetic data generation, with tabular variants such as CTGAN designed for mixed numeric and categorical columns.

When to use Generative Adversarial Network (GAN)

• Generating synthetic samples that resemble a real dataset
• Augmenting a small or imbalanced training set
• Sharing data-like samples where original records cannot be released

When to use something else

• A simple rebalancing of classes; use SMOTE or resampling
• Stable training and likelihood estimates are needed; use variational autoencoders or other likelihood-based generators

Assumptions and requirements

• Sufficient training data to learn the distribution
• Feature scaling and encoding of categorical columns
• Monitoring for mode collapse and unstable training
• Synthetic data does not by itself guarantee privacy

What the analysis reports

• Generator and discriminator loss curves
• Synthetic samples
• Distribution comparisons between real and synthetic data
• Utility of synthetic data for downstream models

How to report it

Synthetic data generated by the GAN reproduced the marginal distributions of the real data, and a model trained on synthetic data achieved an AUC of [value] on real test data.

Related methods

• Variational Autoencoder
• SMOTE
• Diffusion Models

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