Machine learningMachine learning
集成自监督学习
集成自监督学习将多个自监督模型、目标或增强视图组合到一个统一的框架中，以从无标签数据中生成更鲁棒、更具泛化性的表示。通过聚合多样化的自监督信号，集成模型降低了表示坍塌的风险，并在下游任务上优于单一目标自监督学习方法。
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来源
- Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Ávila Pires, B., Guo, Z., Gheshlaghi Azar, M., Piot, B., Kavukcuoglu, K., Munos, R., & Valko, M. (2020). Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning. Advances in Neural Information Processing Systems, 33, 21271–21284. link ↗
- Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging Properties in Self-Supervised Vision Transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 9650–9660. DOI: 10.1109/ICCV48922.2021.00951 ↗
如何引用本页
ScholarGate. (2026, June 3). Ensemble Self-supervised Learning (Combining Multiple Self-supervised Models or Objectives). ScholarGate. https://scholargate.app/zh/machine-learning/ensemble-self-supervised-learning
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