方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 数据增强 (Data Augmentation)× | 生成对抗网络× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2019 | 2014 |
| 提出者≠ | Connor Shorten & Taghi Khoshgoftaar | Goodfellow, I. et al. |
| 类型≠ | Regularization / data preprocessing technique | Generative deep learning (adversarial two-network game) |
| 开创性文献≠ | Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6, 60. DOI ↗ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ |
| 别名 | Training Data Augmentation, Image Augmentation, Veri Artırma, Synthetic Data Augmentation | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network |
| 相关≠ | 2 | 4 |
| 摘要≠ | Data augmentation is a family of techniques that artificially expands a training dataset by applying label-preserving transformations to existing samples. Originally systematized for image classification tasks, it is now applied broadly across vision, text, audio, and tabular domains. It emerged as a practical answer to the chronic scarcity of labeled data in supervised deep learning and remains a standard preprocessing step in modern neural network pipelines. | A Generative Adversarial Network (GAN), introduced by Ian Goodfellow and colleagues in 2014, produces realistic synthetic data through the competition of two neural networks — a generator and a discriminator. It is widely used for image synthesis, data augmentation, and distribution estimation. |
| ScholarGate数据集 ↗ |
|
|