方法证据记录
Self-supervised Logistic Regression
Self-supervised logistic regression is a two-stage pipeline in which a neural encoder is first trained on abundant unlabeled data through a self-supervised pretext task — such as contrastive learning or masked prediction — and then the frozen learned representations are classified with a standard logistic regression model trained on a small labeled dataset. This linear evaluation protocol is widely used to benchmark the quality of self-supervised representations.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Self-supervised Representation Learning with Logistic Regression Classifier
分类方法记录 · ml-model / machine-learning
- 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. · URL
- 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
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