# Vanishing Twin GAN: How training a weak Generative Adversarial Network can improve semi-supervised image classification

Source: [Vanishing Twin GAN: How training a weak Generative Adversarial Network can improve semi-supervised image classification](<https://www.sota2.com/research/paper/68fa69771e209131cea22421>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:00:47.140Z

Authors: Saman Motamed, Farzad Khalvati

Published: 2021-03-03T16:08:27.000Z

## Abstract

Generative Adversarial Networks can learn the mapping of random noise to realistic images in a semi-supervised framework. This mapping ability can be used for semi-supervised image classification to detect images of an unknown class where there is no training data to be used for supervised classification. However, if the unknown class shares similar characteristics to the known class(es), GANs can learn to generalize and generate images that look like both classes. This generalization ability can hinder the classification performance. In this work, we propose the Vanishing Twin GAN. By training a weak GAN and using its generated output image parallel to the regular GAN, the Vanishing Twin training improves semi-supervised image classification where image similarity can hurt classification tasks.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2103.02496>)

[PDF](<https://arxiv.org/pdf/2103.02496.pdf>)