SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning 論文筆記
前言 目前大多數小樣本學習器首先使用一個卷積網絡提取圖像特徵，然後將元學習方法與最近鄰分類器結合起來，以進行圖像識別。本文探討了這樣一種可能性，即在不使用元學習方法，而僅使用最近鄰分類器的情況下，能否很好地處理小樣本學習問題。 本文發現，對圖像特徵進行簡單的特徵轉換，然後再進行最近鄰分類，也可以產生很好的小樣本學習結果。比如，使用DenseNet特徵的最近鄰分類器，在結合均值相減（mean sub
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相關文章
- 1. 論文閱讀筆記《SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning》
- 2. 【Active Learning - 01】 2013_CVPR_Adaptive Active Learning for Image Classification 論文筆記
- 3. 論文筆記-recurrent CNN for text classification
- 4. 論文閱讀筆記《Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning》
- 5. 論文筆記--ActionVLAD: Learning spatio-temporal aggregation for action classification
- 6. 論文筆記《Cross-Language Learning for Program Classification using Bilateral Tree-Based Convolutional Ne
- 7. Dense Classification and Implanting for Few-Shot Learning論文筆記
- 8. 論文閱讀筆記 | (ECCV 2018) Learning to Navigate for Fine-grained Classification
- 9. 論文筆記《Active Convolution: Learning the Shape of Convolution for Image Classification》
- 10. 論文閱讀筆記《Deep Active Learning for Civil Infrastructure Defect Detection and Classification》