Сравнение методов

	Визуальное контрастивное обучение	Случайный лес
Область	Глубокое обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2020	2001
Автор метода	Chen, T. et al. (SimCLR); He, K. et al. (MoCo)	Breiman, L.
Тип	Self-supervised deep representation learning	Ensemble (bagging of decision trees)
Основополагающий источник	Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. ICML.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Другие названия	Karşıtlık Öğrenmesi — Görsel (SimCLR / MoCo / BYOL), contrastive learning, self-supervised visual representation learning, SimCLR	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Связанные	5	4
Сводка	Visual contrastive learning is a self-supervised deep-learning approach — popularised by frameworks such as SimCLR (Chen et al., 2020) and MoCo (He et al., 2020) — that learns rich image representations without labels by pulling different augmentations of the same image together and pushing different images apart. It turns a large pool of unlabelled images into a useful feature extractor.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.