首页edge-labeling graph neural network for few-shot learning

edge-labeling graph neural network for few-shot learning

时间: 2023-05-03 21:02:29 浏览: 153

Semi Supervised Learning for Few Shot Image to Image Translation

In the last few years, unpaired image-to-image translation has witnessed remarkable progress. Although the latest methods are able to generate realistic images, they crucially rely on a large number of labeled images. Recently, some methods have tackled the challenging setting of few-shot image-to-image translation, reducing the labeled data requirements for the target domain during inference. In this work, we go one step further and reduce the amount of required labeled data also from the source domain during training. To do so, we propose applying semi-supervised learning via a noise-tolerant pseudo-labeling procedure. We also apply a cycle consistency constraint to further exploit the information from unlabeled images, either from the same dataset or external.

B'edge-labeling graph neural network for few-shot learning'是一种用于少样本学习的边标签图神经网络。该网络可用于处理其他神经网络难以解决的少样本学习问题，可以提高学习效果和准确率。

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