# Applications and Effect Evaluation of Generative Adversarial Networks in Semi-Supervised Learning

Source: [Applications and Effect Evaluation of Generative Adversarial Networks in Semi-Supervised Learning](<https://www.sota2.com/research/paper/68fa78c11e209131cea7a4e3>)

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

Data updated: 2025-10-26T04:46:07.791Z

Authors: Jiyu Hu, Haijiang Zeng, Zhen Tian

Published: 2025-05-26T05:08:16.000Z

## Abstract

In recent years, image classification, as a core task in computer vision, relies on high-quality labelled data, which restricts the wide application of deep learning models in practical scenarios. To alleviate the problem of insufficient labelled samples, semi-supervised learning has gradually become a research hotspot. In this paper, we construct a semi-supervised image classification model based on Generative Adversarial Networks (GANs), and through the introduction of the collaborative training mechanism of generators, discriminators and classifiers, we achieve the effective use of limited labelled data and a large amount of unlabelled data, improve the quality of image generation and classification accuracy, and provide an effective solution for the task of image recognition in complex environments.

## 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/2505.19522>)

[PDF](<https://arxiv.org/pdf/2505.19522.pdf>)