用生成对抗网络提升小样本图像分类效果
Applications and Effect Evaluation of Generative Adversarial Networks in Semi-Supervised Learning
- 构建基于GAN的半监督分类模型，协同训练生成器、判别器和分类器
- 在有限标注数据下显著提升图像分类准确率和生成质量
- 适合标注数据稀缺场景下的图像识别任务
近年来，图像分类作为计算机视觉的核心任务，依赖高质量标注数据，限制了深度学习模型在实际场景中的广泛应用。为缓解标注样本不足的问题，半监督学习逐渐成为研究热点。本文构建了一种基于生成对抗网络（GAN）的半监督图像分类模型，通过引入生成器、判别器与分类器的协同训练机制，有效利用少量标注数据和大量未标注数据，提升了图像生成质量和分类准确率，为复杂环境下图像识别任务提供了有效解决方案。
原文摘要 · Abstract (English)
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.
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