Matching Networks for one Shot Learning 阅读笔记
Conclusion parametric vs metric karpathy很好的paper notesgit Advantages 使用加权平均(metric)方法去作few-shot,优势: 速度快，须要训练量小 将对S的训练加入模型 pθ(y|x,S) p θ ( y | x , S ) ，具体作法是:分红task(或称为训练集)去训练, 每次选择一些类别L(label sets), 而
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相关文章
- 1. 论文《Matching Networks for One Shot Learning》阅读
- 2. Matching Networks for One Shot Learning
- 3. 【One Shot】《Matching Networks for One Shot Learning》
- 4. Matching Networks for One Shot Learning论文解读
- 5. 论文阅读笔记《Memory Matching Networks for One-Shot Image Recognition》
- 6. Matching Networks for One Shot Learning论文分析
- 7. 论文阅读笔记《Image Deformation Meta-Networks for One-Shot Learning》
- 8. 论文阅读笔记《Generative Adversarial Residual Pairwise Networks for One Shot Learning 》
- 9. 论文阅读笔记《Active One-shot Learning》
- 10. 论文阅读笔记《Siamese Neural Networks for One-shot Image Recognition》