Machine learningMachine learning
在线自监督学习
在线自监督学习（online SSL）在没有人类标签的情况下，利用自动生成的监督信号（代理任务）在按顺序到达或流式传输的无标签数据上训练神经网络。通过在新的数据流入时持续更新模型，它能够在不存储完整数据集的情况下实现不断演化的表示——这对于实时系统、边缘设备和隐私受限的场景至关重要。
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来源
- Gidaris, S., Bursuc, A., Komodakis, N., Perez, P., & Cord, M. (2021). OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 6830–6840. link ↗
- Fini, E., Da Costa, V. G. T., Alameda-Pineda, X., Ricci, E., Alahari, K., & Mairal, J. (2022). Self-Supervised Models are Continual Learners. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9621–9630. link ↗
如何引用本页
ScholarGate. (2026, June 3). Online Self-supervised Learning (Continual Self-supervised Representation Learning from Streaming Data). ScholarGate. https://scholargate.app/zh/machine-learning/online-self-supervised-learning
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