השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| למידת-מעט-דוגמאות מרוּגֶלֶת× | למידת מעט דוגמאות× | |
|---|---|---|
| תחום | למידת מכונה | למידת מכונה |
| משפחה | Machine learning | Machine learning |
| שנת המקור≠ | 2016-2020 | 2011–2017 |
| הוגה השיטה≠ | Multiple (Chen et al., Tian et al., Snell et al., and others) | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| סוג≠ | Meta-learning framework with explicit regularization | Meta-learning / low-data learning paradigm |
| מקור מכונן≠ | 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 ↗ |
| כינויים | 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 |
| קשורות≠ | 5 | 4 |
| תקציר≠ | 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. |
| ScholarGateמערך נתונים ↗ |
|
|