[小樣本學習] 論文筆記 Learning to Self-Train for Semi-Supervised Few-Shot Classification
學習自我訓練的半監督小樣本分類 Li-nips2019-LST-Learning to Self-Train for Semi-Supervised Few-Shot Classification 摘要 作者提出了一種新穎的半監督元學習方法：學習自我訓練（簡稱 LST）。這種方法利用無標籤數據，特別是學習如何擇優挑選和標記這些無標籤數據，以進一步提高性能。對於每個小樣本任務，訓練一個小樣本模型來預
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相關文章
- 1. 論文閱讀筆記《Learning to Select Base Classes for Few-shot Classification》
- 2. 論文閱讀筆記 | (ECCV 2018) Learning to Navigate for Fine-grained Classification
- 3. 【Active Learning - 01】 2013_CVPR_Adaptive Active Learning for Image Classification 論文筆記
- 4. 【論文筆記】Representation Learning by Learning to Count
- 5. Learning to Navigate for Fine-grained Classification 論文學習
- 6. 小樣本學習 | Learning to Compare: Relation Network for Few-Shot Learning
- 7. Learning a Deep ConvNet for Multi-label Classification with Partial Labels 論文筆記
- 8. Learning to Paraphrase for Question Answering 論文筆記
- 9. SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning 論文筆記
- 10. 論文閱讀筆記《SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning》