Machine learningMachine learning
自监督度量学习
自监督度量学习训练一个神经网络编码器，将输入嵌入到向量空间中，使得语义相似的项目彼此靠近，它使用自动生成的伪标签而非人工标注。通过将自监督的“借口任务”与对比学习或三元组度量目标相结合，可以生成可迁移、标签效率高的表示，适用于检索、聚类和少样本分类。
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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 2020), PMLR 119, 1597–1607. link ↗
- Khosla, P., Tian, Y., Wang, X., Liu, C., Krishnan, D., Isola, P., & Tian, Y. (2020). Supervised Contrastive Learning. Advances in Neural Information Processing Systems (NeurIPS 2020), 33, 18661–18673. link ↗
如何引用本页
ScholarGate. (2026, June 3). Self-supervised Metric Learning. ScholarGate. https://scholargate.app/zh/machine-learning/self-supervised-metric-learning
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