Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Ujifunzaji wa Kishoti Kidogo Ulioratibiwa× | Kujifunza kwa Kiasi Kidogo cha Mifano× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Mashine | Ujifunzaji wa Mashine |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2016-2020 | 2011–2017 |
| Mwanzilishi≠ | Multiple (Chen et al., Tian et al., Snell et al., and others) | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| Aina≠ | Meta-learning framework with explicit regularization | Meta-learning / low-data learning paradigm |
| Chanzo asilia≠ | Chen, W., Liu, Y., Kira, Z., Wang, Y. F., & Huang, J. (2019). A Closer Look at Few-Shot Classification. International Conference on Learning Representations (ICLR). link ↗ | Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., & Kavukcuoglu, K. (2016). Matching Networks for One Shot Learning. Advances in Neural Information Processing Systems (NeurIPS), 29. link ↗ |
| Majina mbadala | FSL with regularization, regularized meta-learning, few-shot learning with regularization, regularized episodic learning | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| Zinazohusiana≠ | 5 | 4 |
| Muhtasari≠ | Regularized few-shot learning augments standard few-shot learning pipelines with explicit regularization mechanisms — such as weight decay, dropout, data augmentation, label smoothing, or manifold constraints — to reduce overfitting to the tiny support sets that define each episode. This produces more generalizable models when only one to thirty labeled examples per class are available. | Few-shot learning is a machine learning paradigm that trains models to recognize new classes or solve new tasks from only a handful of labeled examples — typically one to five — by leveraging prior knowledge acquired from a large, related training distribution. It is especially relevant in domains where labeling is expensive, scarce, or structurally limited. |
| ScholarGateSeti ya data ↗ |
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