[小样本学习] 论文笔记 Learning to Self-Train for Semi-Supervised Few-Shot Classification
学习自我训练的半监督小样本分类 Li-nips2019-LST-Learning to Self-Train for Semi-Supervised Few-Shot Classification 摘要 作者提出了一种新颖的半监督元学习方法：学习自我训练（简称 LST）。这种方法利用无标签数据，特别是学习如何择优挑选和标记这些无标签数据，以进一步提高性能。对于每个小样本任务，训练一个小样本模型来预
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相关文章
- 1. 论文阅读笔记《Learning to Select Base Classes for Few-shot Classification》
- 2. 论文阅读笔记 | (ECCV 2018) Learning to Navigate for Fine-grained Classification
- 3. 【Active Learning - 01】 2013_CVPR_Adaptive Active Learning for Image Classification 论文笔记
- 4. 【论文笔记】Representation Learning by Learning to Count
- 5. Learning to Navigate for Fine-grained Classification 论文学习
- 6. 小样本学习 | Learning to Compare: Relation Network for Few-Shot Learning
- 7. Learning a Deep ConvNet for Multi-label Classification with Partial Labels 论文笔记
- 8. Learning to Paraphrase for Question Answering 论文笔记
- 9. SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning 论文笔记
- 10. 论文阅读笔记《SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning》