Machine learningMachine learning
自监督 K-近邻
自监督 K-近邻（SSL-kNN）结合了无标签的表示学习与非参数 k-NN 分类器。首先通过自监督目标（如对比学习或掩码预测）训练一个神经编码器，使其语义相似的样本在嵌入空间中聚集。然后，在这些嵌入上进行简单的 k-NN 查询即可分配类别标签，既作为轻量级探针，也作为实际分类器。
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来源
- Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119, 1597–1607. link ↗
- Wu, Z., Xiong, Y., Yu, S. X., & Lin, D. (2018). Unsupervised feature learning via non-parametric instance discrimination. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 3733–3742. DOI: 10.1109/CVPR.2018.00393 ↗
如何引用本页
ScholarGate. (2026, June 3). Self-supervised K-Nearest Neighbors (SSL-kNN). ScholarGate. https://scholargate.app/zh/machine-learning/self-supervised-k-nearest-neighbors
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