Machine learningMachine learning
自监督少样本学习
自监督少样本学习（SSL-FSL）将大规模无标注语料库上的自监督预训练与少样本元学习相结合，使模型能够仅从少量标注样本中识别新类别。通过在没有昂贵标注的情况下学习丰富、可迁移的表示，SSL-FSL 解决了监督少样本方法的基本瓶颈：需要大规模标注支持数据。
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来源
- Gidaris, S., Bursuc, A., Komodakis, N., Perez, P., & Cord, M. (2019). Boosting Few-Shot Visual Learning with Self-Supervision. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 8059–8068. DOI: 10.1109/ICCV.2019.00815 ↗
- Su, J.-C., Maji, S., & Hariharan, B. (2020). When Does Self-Supervision Improve Few-Shot Learning? European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 12371, 645–660. DOI: 10.1007/978-3-030-58571-6_38 ↗
如何引用本页
ScholarGate. (2026, June 3). Self-supervised Few-shot Learning (SSL-FSL). ScholarGate. https://scholargate.app/zh/machine-learning/self-supervised-few-shot-learning
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