# Mining GOLD Samples for Conditional GANs

arXiv: https://arxiv.org/abs/1910.09170 · HTML: https://mlsanity.com/paper/1910.09170/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2019.

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

- [High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditions](https://mlsanity.com/paper/2206.14180/) (2022)
- [Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks](https://mlsanity.com/paper/2102.12033/) (2021)
- [Deep Data Augmentation for Weed Recognition Enhancement: A Diffusion Probabilistic Model and Transfer Learning Based Approach](https://mlsanity.com/paper/2210.09509/) (2022)
- [Efficient Subsampling of Realistic Images From GANs Conditional on a Class or a Continuous Variable](https://mlsanity.com/paper/2103.11166/) (2021)
- [Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling](https://mlsanity.com/paper/2212.02090/) (2022)
- [A Unified Framework for Generative Data Augmentation: A Comprehensive Survey](https://mlsanity.com/paper/2310.00277/) (2023)
- [Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative Adversarial Networks](https://mlsanity.com/paper/2404.13634/) (2024)
- [PFGuard: A Generative Framework with Privacy and Fairness Safeguards](https://mlsanity.com/paper/2410.02246/) (2024)
- [Stay Fair! Ensuring Group Fairness in Diffusion Models Across Guidance Scales](https://mlsanity.com/paper/2605.28036/) (2026)
- [Mining Multi-Label Samples from Single Positive Labels](https://mlsanity.com/paper/2206.05764/) (2022)
- [Unleashing the Potential of Synthetic Images: A Study on Histopathology Image Classification](https://mlsanity.com/paper/2409.16002/) (2024)

## Abstract (arXiv)

Conditional generative adversarial networks (cGANs) have gained a considerable attention in recent years due to its class-wise controllability and superior quality for complex generation tasks. We introduce a simple yet effective approach to improving cGANs by measuring the discrepancy between the data distribution and the model distribution on given samples. The proposed measure, coined the gap of log-densities (GOLD), provides an effective self-diagnosis for cGANs while being efficienty computed from the discriminator. We propose three applications of the GOLD: example re-weighting, rejection sampling, and active learning, which improve the training, inference, and data selection of cGANs, respectively. Our experimental results demonstrate that the proposed methods outperform corresponding baselines for all three applications on different image datasets.