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

TL;DR

This paper explores how Generative Adversarial Networks can be used in semi-supervised learning to improve image classification accuracy with limited labeled data and abundant unlabeled data.

Contribution

It introduces a collaborative training mechanism involving generators, discriminators, and classifiers to enhance semi-supervised image classification.

Findings

Improved image classification accuracy in semi-supervised settings

Enhanced quality of generated images

Effective utilization of limited labeled data

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.