方法对比
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| 数据增强 (Data Augmentation)× | 迁移学习× | |
|---|---|---|
| 领域≠ | 深度学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2019 | 2010 (formalized); 1990s (early roots) |
| 提出者≠ | Connor Shorten & Taghi Khoshgoftaar | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| 类型≠ | Regularization / data preprocessing technique | Learning paradigm |
| 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 ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| 别名 | Training Data Augmentation, Image Augmentation, Veri Artırma, Synthetic Data Augmentation | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| 相关≠ | 2 | 3 |
| 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. | Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond. |
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