用生成对抗网络增强稀缺手写和医学图像数据，提升分类效果
Cross-Domain Adversarial Augmentation: Stabilizing GANs for Medical and Handwriting Data Scarcity
- 基于DCGAN生成合成数据，提升低资源场景下的数据多样性
- 合成数据使分类准确率在少样本条件下显著提高
- 方法简单可复现，适合医疗与手写识别等数据匮乏领域
生成对抗网络（GAN）可通过生成额外训练样本缓解计算机视觉任务中的数据稀缺问题。本文在两个低资源领域——孟加拉文手写字符识别与胸部X光图像分析中探索生成式数据增强。采用基于DCGAN的模型在64x64图像上训练生成合成样本，并通过Inception Score（IS）、Fréchet Inception Distance（FID）及t-SNE、UMAP可视化评估生成质量。为验证实际价值，使用真实数据与真实+合成数据联合训练图像分类器。实验表明，合成数据增强提升了数据多样性，并在小样本设置下持续提高分类性能。我们还研究了梯度惩罚与谱归一化等训练稳定技术，对合成到真实数据比例及样本筛选策略进行消融分析。同时讨论了医学图像评估挑战、数据集许可及合成数据隐私问题。该方法简洁可复现，为资源受限成像应用提供了强基线。
原文摘要 · Abstract (English)
Generative Adversarial Networks (GANs) can help overcome data scarcity in computer vision tasks by generating additional training samples. In this work, we explore generative data augmentation in two low-resource domains: Bangla handwritten character recognition and chest X-ray image analysis. We use DCGAN-based models trained on 64x64 images to generate synthetic samples and evaluate their quality using Inception Score (IS), Fréchet Inception Distance (FID), and visualization methods such as t-SNE and UMAP. To measure practical usefulness, we train image classifiers using real data and a combination of real and synthetic data. Experimental results show that synthetic augmentation improves data diversity and consistently increases classification performance in limited-data settings. We also investigate training stability techniques, including gradient penalty and spectral normalization, and perform ablation studies on synthetic-to-real data ratios and sample filtering strategies. In addition, we discuss challenges related to medical image evaluation, dataset licensing, and privacy concerns of synthetic data. Our approach is simple, reproducible, and provides a strong baseline for generative augmentation in resource-constrained imaging applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。