Transductive Unbiased Embedding for Zero-Shot Learning阅读笔记

时间 2021-01-11

标签 cvpr2018 繁體版

原文原文链接

Transductive Unbiased Embedding for Zero-Shot Learning Summary PRO ubias term: 在Loss添加一个针对未知类的loss, 部分抑制了zero shot天生倾向于带label数据的问题巧妙的数据利用，虽然target dataset没有用label(图片文字对应关系)，但是用了label的文字embedding 对CNN

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1. [CVPR 18] Transductive Unbiased Embedding for Zero-Shot Learning
2. 【论文学习笔记】Transductive Unbiased Embedding for Zero-Shot Learning (2018_CVPR)
3. 论文阅读笔记《Learning Embedding Adaptation for Few-Shot Learning》
4. 论文阅读笔记《Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning》
5. 论文阅读笔记《Learning to propagate labels: Transductive propagation network for few-shot learning》
6. FeUdal Networks for Hierarchical Reinforcement Learning 阅读笔记
7. 论文阅读笔记《Instance-Level Embedding Adaptation for Few-Shot Learning》
8. NASE Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture Search 阅读笔记
9. Transfer Learning in a Transductive Setting阅读笔记NIPS2013
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1. [CVPR 18] Transductive Unbiased Embedding for Zero-Shot Learning
2. 【论文学习笔记】Transductive Unbiased Embedding for Zero-Shot Learning (2018_CVPR)
3. 论文阅读笔记《Learning Embedding Adaptation for Few-Shot Learning》
4. 论文阅读笔记《Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning》
5. 论文阅读笔记《Learning to propagate labels: Transductive propagation network for few-shot learning》
6. FeUdal Networks for Hierarchical Reinforcement Learning 阅读笔记
7. 论文阅读笔记《Instance-Level Embedding Adaptation for Few-Shot Learning》
8. NASE Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture Search 阅读笔记
9. Transfer Learning in a Transductive Setting阅读笔记NIPS2013
10. Matching Networks for one Shot Learning 阅读笔记

>>更多相关文章<<