Machine learningDeep learning / NLP / CV
自监督图像分类
自监督图像分类通过解决代理任务（例如，预测同一图像的两个增强视图的相似性）在大规模无标签图像数据集上训练深度视觉编码器，然后仅在有标签示例上微调轻量级分类器头。它由 SimCLR 和 MoCo 等框架在 2020 年左右开创，极大地减少了对昂贵手动标注的需求，同时实现了可与完全监督模型相媲美的准确性。
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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), PMLR 119, 1597–1607. link ↗
- He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020). Momentum Contrast for Unsupervised Visual Representation Learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9729–9738. DOI: 10.1109/CVPR42600.2020.00975 ↗
如何引用本页
ScholarGate. (2026, June 3). Self-supervised Learning for Image Classification. ScholarGate. https://scholargate.app/zh/deep-learning/self-supervised-image-classification
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