方法对比
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| 半监督变分自编码器× | 半监督卷积神经网络× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2014 | 2013–2017 |
| 提出者≠ | Kingma, D. P.; Mohamed, S.; Rezende, D. J.; Wierstra, D. | Lee, D.-H.; Tarvainen, A. & Valpola, H. (among others) |
| 类型≠ | Generative probabilistic model (semi-supervised) | Semi-supervised deep learning |
| 开创性文献≠ | Kingma, D. P., Mohamed, S., Rezende, D. J., & Wierstra, D. (2014). Semi-supervised learning with deep generative models. Advances in Neural Information Processing Systems (NeurIPS), 27, 3581–3589. link ↗ | Lee, D.-H. (2013). Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. ICML Workshop on Challenges in Representation Learning. link ↗ |
| 别名 | Semi-supervised VAE, M2 model, VAE with label propagation, deep generative semi-supervised model | SSL-CNN, semi-supervised CNN, self-training CNN, pseudo-label CNN |
| 相关≠ | 6 | 5 |
| 摘要≠ | The semi-supervised VAE (M2 model) is a deep generative method that jointly learns a latent representation of inputs and a classifier, leveraging both labeled and unlabeled examples in a principled probabilistic framework. Introduced by Kingma et al. in 2014, it allows accurate classification even when labels are scarce by having the generative model explain away unlabeled observations. | A Semi-supervised CNN trains a convolutional network on a small labeled image set and a larger pool of unlabeled images simultaneously, using techniques such as pseudo-labeling and consistency regularization to extract supervisory signal from unlabeled data. This strategy closes much of the performance gap caused by scarce annotations without requiring additional human labeling effort. |
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