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
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