方法对比

	数据增强 (Data Augmentation)	分布外检测
领域	深度学习	机器学习
方法族	Machine learning	Machine learning
起源年份	2019	2017
提出者	Connor Shorten & Taghi Khoshgoftaar	Hendrycks & Gimpel
类型	Regularization / data preprocessing technique	Reliability and safety method for neural networks
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.	Hendrycks, D., & Gimpel, K. (2017). A baseline for detecting misclassified and out-of-distribution examples in neural networks. International Conference on Learning Representations.
别名	Training Data Augmentation, Image Augmentation, Veri Artırma, Synthetic Data Augmentation	OOD Detection, Novelty Detection, Open-Set Recognition, Dağılım Dışı Tespit
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.	Out-of-Distribution (OOD) detection is a set of techniques that identify when a deployed machine learning model receives inputs that differ significantly from its training data distribution. Introduced as a formal problem by Hendrycks and Gimpel in 2017, these methods enable models to flag unfamiliar inputs rather than silently produce unreliable predictions, making them foundational to trustworthy and safe AI deployment in high-stakes domains.