# Multi-class Generative Adversarial Nets for Semi-supervised Image Classification

arXiv: https://arxiv.org/abs/2102.06944 · HTML: https://mlsanity.com/paper/2102.06944/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 7 references that are in the map.
- First posted on arXiv 13 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray](https://mlsanity.com/paper/2010.06418/) (2020)

## Built on by (most cited first, 1 of 1)

- [Vanishing Twin GAN: How training a weak Generative Adversarial Network can improve semi-supervised image classification](https://mlsanity.com/paper/2103.02496/) (2021)

## Abstract (arXiv)

From generating never-before-seen images to domain adaptation, applications of Generative Adversarial Networks (GANs) spread wide in the domain of vision and graphics problems. With the remarkable ability of GANs in learning the distribution and generating images of a particular class, they can be used for semi-supervised classification tasks. However, the problem is that if two classes of images share similar characteristics, the GAN might learn to generalize and hinder the classification of the two classes. In this paper, we use various images from MNIST and Fashion-MNIST datasets to illustrate how similar images cause the GAN to generalize, leading to the poor classification of images. We propose a modification to the traditional training of GANs that allows for improved multi-class classification in similar classes of images in a semi-supervised learning framework.