Machine learningMachine learning
自监督逻辑回归
自监督逻辑回归是一个两阶段流程：首先，通过对比学习或掩码预测等自监督预任务，在大量无标签数据上训练一个神经网络编码器；然后，使用在少量有标签数据集上训练的标准逻辑回归模型对冻结的已学习表征进行分类。这种线性评估协议被广泛用于衡量自监督表征的质量。
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Method map
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来源
- Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. Proceedings of the 37th International Conference on Machine Learning (ICML), 1597–1607. link ↗
- van Engelen, J. E., & Hoos, H. H. (2020). A survey on semi-supervised learning. Machine Learning, 109(2), 373–440. DOI: 10.1007/s10994-019-05855-6 ↗
如何引用本页
ScholarGate. (2026, June 3). Self-supervised Representation Learning with Logistic Regression Classifier. ScholarGate. https://scholargate.app/zh/machine-learning/self-supervised-logistic-regression
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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