Machine learningDeep learning / NLP / CV
弱监督生成对抗网络 (Weakly Supervised GAN)
弱监督生成对抗网络 (Weakly Supervised GAN) 是一种生成对抗网络，它使用部分标记、噪声标记或粗粒度标注数据进行训练，而不是完全标注的真实数据。它扩展了标准的 GAN 框架，使得有限的监督能够指导条件生成或判别学习，从而在标签稀缺的情况下实现高质量的数据合成和分类。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- Odena, A., Olah, C., & Shlens, J. (2017). Conditional Image Synthesis with Auxiliary Classifier GANs. Proceedings of the 34th International Conference on Machine Learning (ICML), PMLR 70, 2642–2651. link ↗
- Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems (NeurIPS), 27. link ↗
如何引用本页
ScholarGate. (2026, June 3). Weakly Supervised Generative Adversarial Network. ScholarGate. https://scholargate.app/zh/deep-learning/weakly-supervised-gan
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 扩散模型深度学习↔ compare
- 生成对抗网络深度学习↔ compare
- Semi-supervised GAN深度学习↔ compare
- 变分自编码器深度学习↔ compare
- 弱监督图像分类深度学习↔ compare