方法对比
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| 弱监督生成对抗网络 (Weakly Supervised GAN)× | 生成对抗网络× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2014–2017 | 2014 |
| 提出者≠ | Odena et al.; building on Goodfellow et al. (2014) | Goodfellow, I. et al. |
| 类型≠ | Generative model with weak supervision | Generative deep learning (adversarial two-network game) |
| 开创性文献≠ | 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. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ |
| 别名 | WS-GAN, weakly supervised generative adversarial network, label-efficient GAN, semi-labeled GAN | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network |
| 相关≠ | 5 | 4 |
| 摘要≠ | A Weakly Supervised GAN is a generative adversarial network trained with partially labeled, noisily labeled, or coarse-annotation data instead of fully annotated ground truth. It extends the standard GAN framework so that limited supervision guides conditional generation or discriminative learning, enabling high-quality data synthesis and classification in label-scarce settings. | A Generative Adversarial Network (GAN), introduced by Ian Goodfellow and colleagues in 2014, produces realistic synthetic data through the competition of two neural networks — a generator and a discriminator. It is widely used for image synthesis, data augmentation, and distribution estimation. |
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