SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning 论文笔记
前言 目前大多数小样本学习器首先使用一个卷积网络提取图像特征，然后将元学习方法与最近邻分类器结合起来，以进行图像识别。本文探讨了这样一种可能性，即在不使用元学习方法，而仅使用最近邻分类器的情况下，能否很好地处理小样本学习问题。 本文发现，对图像特征进行简单的特征转换，然后再进行最近邻分类，也可以产生很好的小样本学习结果。比如，使用DenseNet特征的最近邻分类器，在结合均值相减（mean sub
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相关文章
- 1. 论文阅读笔记《SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning》
- 2. 【Active Learning - 01】 2013_CVPR_Adaptive Active Learning for Image Classification 论文笔记
- 3. 论文笔记-recurrent CNN for text classification
- 4. 论文阅读笔记《Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning》
- 5. 论文笔记--ActionVLAD: Learning spatio-temporal aggregation for action classification
- 6. 论文笔记《Cross-Language Learning for Program Classification using Bilateral Tree-Based Convolutional Ne
- 7. Dense Classification and Implanting for Few-Shot Learning论文笔记
- 8. 论文阅读笔记 | (ECCV 2018) Learning to Navigate for Fine-grained Classification
- 9. 论文笔记《Active Convolution: Learning the Shape of Convolution for Image Classification》
- 10. 论文阅读笔记《Deep Active Learning for Civil Infrastructure Defect Detection and Classification》