論文閱讀筆記《SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning》
核心思想 本文提出一種基於最近鄰方法的小樣本學習算法（SimpleShot），作者指出目前大量的小樣本學習算法都採用了元學習的方案，而作者卻發現使用簡單的特徵提取器+最近鄰分類器的方法就能實現非常優異的小樣本分類效果。本文首先用特徵提取網絡 f θ f_{\theta} fθ+線性分類器在一個基礎數據集上對網絡進行訓練，將訓練得到的特徵提取網絡增加一個簡單的特徵變換（中心化和L2規範化），再
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相關文章
- 1. SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning 論文筆記
- 2. Machine Learning & Deep Learning 論文閱讀筆記
- 3. 論文閱讀筆記 | (ECCV 2018) Learning to Navigate for Fine-grained Classification
- 4. 論文閱讀筆記《Deep Active Learning for Civil Infrastructure Defect Detection and Classification》
- 5. 論文閱讀筆記《Meta-learning for semi-supervised few-shot classification》
- 6. 論文閱讀筆記《Dense Classification and Implanting for Few-Shot Learning》
- 7. 論文閱讀筆記《Learning to Select Base Classes for Few-shot Classification》
- 8. 論文閱讀筆記《Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning》
- 9. 論文閱讀：Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
- 10. 論文閱讀筆記《MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification》