# Enhanced Balancing GAN: Minority-class Image Generation

arXiv: https://arxiv.org/abs/2011.00189 · HTML: https://mlsanity.com/paper/2011.00189/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2020.

## Built on by (most cited first, 15 of 15)

- [Diffusion Models for Image Restoration and Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2308.09388/) (2023)
- [Don't Play Favorites: Minority Guidance for Diffusion Models](https://mlsanity.com/paper/2301.12334/) (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)
- [SpoofGAN: Synthetic Fingerprint Spoof Images](https://mlsanity.com/paper/2204.06498/) (2022)
- [Anomaly Detection in Additive Manufacturing Processes using Supervised Classification with Imbalanced Sensor Data based on Generative Adversarial Network](https://mlsanity.com/paper/2210.17274/) (2022)
- [Boost-and-Skip: A Simple Guidance-Free Diffusion for Minority Generation](https://mlsanity.com/paper/2502.06516/) (2025)
- [Evaluating the Quality and Diversity of DCGAN-based Generatively Synthesized Diabetic Retinopathy Imagery](https://mlsanity.com/paper/2208.05593/) (2022)
- [A Distributed Approach to Meteorological Predictions: Addressing Data Imbalance in Precipitation Prediction Models through Federated Learning and GANs](https://mlsanity.com/paper/2310.13161/) (2023)
- [Evaluating Generic Auto-ML Tools for Computational Pathology](https://mlsanity.com/paper/2112.03622/) (2021)
- [Conditional Variational Autoencoder with Balanced Pre-training for Generative Adversarial Networks](https://mlsanity.com/paper/2201.04809/) (2022)
- [Adaptive Input-image Normalization for Solving the Mode Collapse Problem in GAN-based X-ray Images](https://mlsanity.com/paper/2309.12245/) (2023)
- [Domain Transfer Through Image-to-Image Translation for Uncertainty-Aware Prostate Cancer Classification](https://mlsanity.com/paper/2307.00479/) (2023)
- [Conformalised data synthesis](https://mlsanity.com/paper/2312.08999/) (2023)
- [Deep Convolutional Neural Networks on Multiclass Classification of Three-Dimensional Brain Images for Parkinson's Disease Stage Prediction](https://mlsanity.com/paper/2410.23649/) (2024)

## Abstract (arXiv)

Generative adversarial networks (GANs) are one of the most powerful generative models, but always require a large and balanced dataset to train. Traditional GANs are not applicable to generate minority-class images in a highly imbalanced dataset. Balancing GAN (BAGAN) is proposed to mitigate this problem, but it is unstable when images in different classes look similar, e.g. flowers and cells. In this work, we propose a supervised autoencoder with an intermediate embedding model to disperse the labeled latent vectors. With the improved autoencoder initialization, we also build an architecture of BAGAN with gradient penalty (BAGAN-GP). Our proposed model overcomes the unstable issue in original BAGAN and converges faster to high quality generations. Our model achieves high performance on the imbalanced scale-down version of MNIST Fashion, CIFAR-10, and one small-scale medical image dataset.