# Sampling Strategies for GAN Synthetic Data

arXiv: https://arxiv.org/abs/1909.04689 · HTML: https://mlsanity.com/paper/1909.04689/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 11 of 11)

- [Utility Theory of Synthetic Data Generation](https://mlsanity.com/paper/2305.10015/) (2023)
- [Visual Transformer for Task-aware Active Learning](https://mlsanity.com/paper/2106.03801/) (2021)
- [Diversity and Diffusion: Observations on Synthetic Image Distributions with Stable Diffusion](https://mlsanity.com/paper/2311.00056/) (2023)
- [Co-supervised learning paradigm with conditional generative adversarial networks for sample-efficient classification](https://mlsanity.com/paper/2212.13589/) (2022)
- [Image-free Domain Generalization via CLIP for 3D Hand Pose Estimation](https://mlsanity.com/paper/2210.16788/) (2022)
- [Human Body Measurement Estimation with Adversarial Augmentation](https://mlsanity.com/paper/2210.05667/) (2022)
- [Label Geometry Aware Discriminator for Conditional Generative Networks](https://mlsanity.com/paper/2105.05501/) (2021)
- [Conformalised data synthesis](https://mlsanity.com/paper/2312.08999/) (2023)
- [SAFLEX: Self-Adaptive Augmentation via Feature Label Extrapolation](https://mlsanity.com/paper/2410.02512/) (2024)
- [Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models](https://mlsanity.com/paper/2507.17008/) (2025)
- [Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting](https://mlsanity.com/paper/2607.02637/) (2026)

## Abstract (arXiv)

Generative Adversarial Networks (GANs) have been used widely to generate large volumes of synthetic data. This data is being utilized for augmenting with real examples in order to train deep Convolutional Neural Networks (CNNs). Studies have shown that the generated examples lack sufficient realism to train deep CNNs and are poor in diversity. Unlike previous studies of randomly augmenting the synthetic data with real data, we present our simple, effective and easy to implement synthetic data sampling methods to train deep CNNs more efficiently and accurately. To this end, we propose to maximally utilize the parameters learned during training of the GAN itself. These include discriminator's realism confidence score and the confidence on the target label of the synthetic data. In addition to this, we explore reinforcement learning (RL) to automatically search a subset of meaningful synthetic examples from a large pool of GAN synthetic data. We evaluate our method on two challenging face attribute classification data sets viz. AffectNet and CelebA. Our extensive experiments clearly demonstrate the need of sampling synthetic data before augmentation, which also improves the performance of one of the state-of-the-art deep CNNs in vitro.