论文阅读-Prototype Rectification for Few-Shot Learning
pdf ECCV 2020 Oral 源码暂未开放 motivation 传统的原型网络是将support集里面每个类的所有样本的特征的平均作为该类的原型representation，通过query集合的特征representation与support集中每个类别的原型representation进行欧式距离计算，在经过softmax得出最后所属类别。 作者认为简单的求平均会产生很大的bias，因
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相关文章
- 1. Prototype Rectification for Few-shot Learning 文献阅读
- 2. Prototype Rectification for Few-Shot Learning
- 3. 论文阅读 Prototypical Networks for Few-shot Learning
- 4. 论文阅读——Dual Learning for Machine Translation
- 5. 论文阅读-《Learning Deep Features for Discriminative Localization》
- 6. 论文阅读：《Wide & Deep Learning for Recommender Systems》
- 7. 【论文阅读】Deep Residual Learning for Image Recognition
- 8. 论文《Matching Networks for One Shot Learning》阅读
- 9. 论文阅读——Deep Residual Learning for Image Recognition
- 10. 论文阅读：Dual Learning for Machine Translation