📄 中文摘要
数据增强是提高机器学习模型性能的重要技术。生成对抗网络（GAN）作为一种强大的生成模型，能够生成高质量的合成数据，从而有效扩展训练数据集。通过对抗训练，GAN 可以学习数据的潜在分布，并生成与真实数据相似的样本。这种方法在图像处理、自然语言处理等领域取得了显著的效果。应用 GAN 进行数据增强不仅可以提高模型的泛化能力，还能在数据稀缺的情况下缓解过拟合问题。研究表明，利用 GAN 生成的合成数据可以显著提升下游任务的性能，成为数据增强领域的重要工具。
使用 GAN 的数据增强
出处: Data Augmentation Using GANs
发布: 2026年3月23日
📄 English Summary
Data Augmentation Using GANs
Data augmentation is a crucial technique for enhancing the performance of machine learning models. Generative Adversarial Networks (GANs), as powerful generative models, can produce high-quality synthetic data to effectively expand training datasets. Through adversarial training, GANs learn the underlying distribution of the data and generate samples that closely resemble real data. This approach has shown significant results in various fields, including image processing and natural language processing. Using GANs for data augmentation not only improves the generalization ability of models but also mitigates overfitting issues in scenarios with scarce data. Research indicates that synthetic data generated by GANs can significantly enhance the performance of downstream tasks, making them an essential tool in the data augmentation domain.