방법 비교

	분포 외 탐지	모델 보정
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2017	2017
창시자	Hendrycks & Gimpel	Platt; Guo et al.
유형	Reliability and safety method for neural networks	Post-hoc probability correction technique
원전	Hendrycks, D., & Gimpel, K. (2017). A baseline for detecting misclassified and out-of-distribution examples in neural networks. International Conference on Learning Representations.	Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. International Conference on Machine Learning, 1321–1330.
별칭	OOD Detection, Novelty Detection, Open-Set Recognition, Dağılım Dışı Tespit	Classifier Calibration, Probability Calibration, Score Calibration, Model Kalibrasyonu
관련	3	3
요약	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.	Model calibration is a post-hoc technique that adjusts the probability outputs of a trained classifier so that predicted confidence scores match empirical outcome frequencies. A classifier is said to be perfectly calibrated if, among all predictions made with confidence p, exactly a fraction p of them are correct. Systematic miscalibration of modern deep neural networks was rigorously documented by Guo et al. (2017), who showed that networks trained with standard cross-entropy loss tend to be overconfident, and proposed temperature scaling as a simple, effective remedy.