论文阅读笔记《Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning》
核心思想 本文提出一种采用图神经网络GNN的小样本学习算法，本质上还是一种学习参数更新策略的元学习算法，使得模型参数能够根据在基础数据集上得到的模型和少量的新样本进行更新，在适应新任务的同时，且不会忘记旧任务。整个网络结构分成特征提取网络 F ( ⋅ ∣ θ ) F(\cdot|\theta) F(⋅∣θ)和分类器网络 C ( ⋅ ∣ w ) C(\cdot|w) C(⋅∣w)两个部分，特征提
相关文章
- 1. 论文阅读笔记《Low-Shot Learning with Imprinted Weights》
- 2. 论文《Learning both Weights and Connections for Efficient Neural Network》阅读笔记
- 3. 论文阅读笔记《Attentive Weights Generation for Few Shot Learning via Information Maximization》
- 4. Document Modeling with Gated Recurrent Neural Network for Sentiment Classification 论文阅读笔记
- 5. 【论文阅读笔记】Document Modeling with Gated Recurrent Neural Network for Sentiment Classification
- 6. 论文阅读笔记《Dense Classification and Implanting for Few-Shot Learning》
- 7. Machine Learning & Deep Learning 论文阅读笔记
- 8. Learning a Deep ConvNet for Multi-label Classification with Partial Labels 论文笔记
- 9. 《Bag of Tricks for Image Classification with Convolutional Neural Networks》论文阅读笔记
- 10. Stacked Denoising Autoencoders (SDAE)
- 更多相关文章...
- • RSS 阅读器 - RSS 教程
- • PHP 实例 - AJAX RSS 阅读器 - PHP教程
- • Tomcat学习笔记（史上最全tomcat学习笔记）
- • JDK13 GA发布：5大特性解读
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. 论文阅读笔记《Low-Shot Learning with Imprinted Weights》
- 2. 论文《Learning both Weights and Connections for Efficient Neural Network》阅读笔记
- 3. 论文阅读笔记《Attentive Weights Generation for Few Shot Learning via Information Maximization》
- 4. Document Modeling with Gated Recurrent Neural Network for Sentiment Classification 论文阅读笔记
- 5. 【论文阅读笔记】Document Modeling with Gated Recurrent Neural Network for Sentiment Classification
- 6. 论文阅读笔记《Dense Classification and Implanting for Few-Shot Learning》
- 7. Machine Learning & Deep Learning 论文阅读笔记
- 8. Learning a Deep ConvNet for Multi-label Classification with Partial Labels 论文笔记
- 9. 《Bag of Tricks for Image Classification with Convolutional Neural Networks》论文阅读笔记
- 10. Stacked Denoising Autoencoders (SDAE)