Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Semi-supervised Variational Autoencoder× | Mtandao wa Mawasiliano wa Nusu-Usindikaji× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Kina | Ujifunzaji wa Kina |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2014 | 2013–2017 |
| Mwanzilishi≠ | Kingma, D. P.; Mohamed, S.; Rezende, D. J.; Wierstra, D. | Lee, D.-H.; Tarvainen, A. & Valpola, H. (among others) |
| Aina≠ | Generative probabilistic model (semi-supervised) | Semi-supervised deep learning |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala | 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 |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | 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. |
| ScholarGateSeti ya data ↗ |
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