# EC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANs

arXiv: https://arxiv.org/abs/2012.15864 · HTML: https://mlsanity.com/paper/2012.15864/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2020.

## Built on by (most cited first, 10 of 10)

- [Effective Data Augmentation With Diffusion Models](https://mlsanity.com/paper/2302.07944/) (2023)
- [AI-Generated Images as Data Source: The Dawn of Synthetic Era](https://mlsanity.com/paper/2310.01830/) (2023)
- [RareGAN: Generating Samples for Rare Classes](https://mlsanity.com/paper/2203.10674/) (2022)
- [Co-supervised learning paradigm with conditional generative adversarial networks for sample-efficient classification](https://mlsanity.com/paper/2212.13589/) (2022)
- [Simulated Data Generation Through Algorithmic Force Coefficient Estimation for AI-Based Robotic Projectile Launch Modeling](https://mlsanity.com/paper/2105.12833/) (2021)
- [3N-GAN: Semi-Supervised Classification of X-Ray Images with a 3-Player Adversarial Framework](https://mlsanity.com/paper/2109.13862/) (2021)
- [Distance Guided Generative Adversarial Network for Explainable Binary Classifications](https://mlsanity.com/paper/2312.17538/) (2023)
- [BEND: Bagging Deep Learning Training Based on Efficient Neural Network Diffusion](https://mlsanity.com/paper/2403.15766/) (2024)
- [SPARSE Data, Rich Results: Few-Shot Semi-Supervised Learning via Class-Conditioned Image Translation](https://mlsanity.com/paper/2508.06429/) (2025)
- [High Cursive Complex Character Recognition using GAN External Classifier](https://mlsanity.com/paper/2509.03062/) (2025)

## Abstract (arXiv)

Semi-supervised learning has been gaining attention as it allows for performing image analysis tasks such as classification with limited labeled data. Some popular algorithms using Generative Adversarial Networks (GANs) for semi-supervised classification share a single architecture for classification and discrimination. However, this may require a model to converge to a separate data distribution for each task, which may reduce overall performance. While progress in semi-supervised learning has been made, less addressed are small-scale, fully-supervised tasks where even unlabeled data is unavailable and unattainable. We therefore, propose a novel GAN model namely External Classifier GAN (EC-GAN), that utilizes GANs and semi-supervised algorithms to improve classification in fully-supervised regimes. Our method leverages a GAN to generate artificial data used to supplement supervised classification. More specifically, we attach an external classifier, hence the name EC-GAN, to the GAN's generator, as opposed to sharing an architecture with the discriminator. Our experiments demonstrate that EC-GAN's performance is comparable to the shared architecture method, far superior to the standard data augmentation and regularization-based approach, and effective on a small, realistic dataset.