# RareGAN: Generating Samples for Rare Classes

arXiv: https://arxiv.org/abs/2203.10674 · HTML: https://mlsanity.com/paper/2203.10674/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 71 references that are in the map.
- First posted on arXiv 20 Mar 2022.

## Builds on (most cited first, 30 of 33)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Conditional Image Synthesis With Auxiliary Classifier GANs](https://mlsanity.com/paper/1610.09585/) (2016)
- [f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization](https://mlsanity.com/paper/1606.00709/) (2016)
- [Reliable Fidelity and Diversity Metrics for Generative Models](https://mlsanity.com/paper/2002.09797/) (2020)
- [Differentiable Augmentation for Data-Efficient GAN Training](https://mlsanity.com/paper/2006.10738/) (2020)
- [Semi-Supervised Learning with Generative Adversarial Networks](https://mlsanity.com/paper/1606.01583/) (2016)
- [Self-Supervised GANs via Auxiliary Rotation Loss](https://mlsanity.com/paper/1811.11212/) (2018)
- [BAGAN: Data Augmentation with Balancing GAN](https://mlsanity.com/paper/1803.09655/) (2018)
- [Good Semi-supervised Learning that Requires a Bad GAN](https://mlsanity.com/paper/1705.09783/) (2017)
- [Generative Models for Effective ML on Private, Decentralized Datasets](https://mlsanity.com/paper/1911.06679/) (2019)
- [How good is my GAN?](https://mlsanity.com/paper/1807.09499/) (2018)
- [Generative Adversarial Minority Oversampling](https://mlsanity.com/paper/1903.09730/) (2019)
- [Do GANs actually learn the distribution? An empirical study](https://mlsanity.com/paper/1706.08224/) (2017)
- [Class Balancing GAN with a Classifier in the Loop](https://mlsanity.com/paper/2106.09402/) (2021)
- [Generative Image Translation for Data Augmentation in Colorectal Histopathology Images](https://mlsanity.com/paper/1910.05827/) (2019)
- [Neural Packet Classification](https://mlsanity.com/paper/1902.10319/) (2019)
- [Teaching a GAN What Not to Learn](https://mlsanity.com/paper/2010.15639/) (2020)
- [EC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANs](https://mlsanity.com/paper/2012.15864/) (2020)
- [Ensembles of Generative Adversarial Networks](https://mlsanity.com/paper/1612.00991/) (2016)
- [SlowFuzz: Automated Domain-Independent Detection of Algorithmic Complexity Vulnerabilities](https://mlsanity.com/paper/1708.08437/) (2017)
- [Active Generative Adversarial Network for Image Classification](https://mlsanity.com/paper/1906.07133/) (2019)
- [MatchGAN: A Self-Supervised Semi-Supervised Conditional Generative Adversarial Network](https://mlsanity.com/paper/2006.06614/) (2020)
- [Adaptive Weighted Discriminator for Training Generative Adversarial Networks](https://mlsanity.com/paper/2012.03149/) (2020)

## Built on by (most cited first, 9 of 9)

- [DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models](https://mlsanity.com/paper/2308.06160/) (2023)
- [Don't Play Favorites: Minority Guidance for Diffusion Models](https://mlsanity.com/paper/2301.12334/) (2023)
- [Benchmarking Algorithmic Bias in Face Recognition: An Experimental Approach Using Synthetic Faces and Human Evaluation](https://mlsanity.com/paper/2308.05441/) (2023)
- [Self-Guided Generation of Minority Samples Using Diffusion Models](https://mlsanity.com/paper/2407.11555/) (2024)
- [Minority-Focused Text-to-Image Generation via Prompt Optimization](https://mlsanity.com/paper/2410.07838/) (2024)
- [Boost-and-Skip: A Simple Guidance-Free Diffusion for Minority Generation](https://mlsanity.com/paper/2502.06516/) (2025)
- [BaNEL: Exploration Posteriors for Generative Modeling Using Only Negative Rewards](https://mlsanity.com/paper/2510.09596/) (2025)
- [Heavy-Tailed Class-Conditional Priors for Long-Tailed Generative Modeling](https://mlsanity.com/paper/2509.02154/) (2025)
- [Visualizing chest X-ray dataset biases using GANs](https://mlsanity.com/paper/2305.00147/) (2023)

## Abstract (arXiv)

We study the problem of learning generative adversarial networks (GANs) for a rare class of an unlabeled dataset subject to a labeling budget. This problem is motivated from practical applications in domains including security (e.g., synthesizing packets for DNS amplification attacks), systems and networking (e.g., synthesizing workloads that trigger high resource usage), and machine learning (e.g., generating images from a rare class). Existing approaches are unsuitable, either requiring fully-labeled datasets or sacrificing the fidelity of the rare class for that of the common classes. We propose RareGAN, a novel synthesis of three key ideas: (1) extending conditional GANs to use labelled and unlabelled data for better generalization; (2) an active learning approach that requests the most useful labels; and (3) a weighted loss function to favor learning the rare class. We show that RareGAN achieves a better fidelity-diversity tradeoff on the rare class than prior work across different applications, budgets, rare class fractions, GAN losses, and architectures.