Сравнение методов

	Аугментация данных	Перенос обучения
Область	Глубокое обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2019	2010 (formalized); 1990s (early roots)
Автор метода	Connor Shorten & Taghi Khoshgoftaar	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Тип	Regularization / data preprocessing technique	Learning paradigm
Основополагающий источник	Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6, 60.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Другие названия	Training Data Augmentation, Image Augmentation, Veri Artırma, Synthetic Data Augmentation	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Связанные	2	3
Сводка	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.