论文标题
通过图卷积网络进行预算了解的几次学习
Budget-aware Few-shot Learning via Graph Convolutional Network
论文作者
论文摘要
本文解决了几乎没有学习的问题，该问题旨在从一些示例中学习新的视觉概念。几次分类的常见问题设置在获取数据标签时假设随机抽样策略，这在实际应用中效率低下。在这项工作中，我们介绍了一个新的预算意识到的少数学习问题，该问题不仅旨在学习新颖的对象类别，而且还需要选择信息的示例来注释以实现数据效率。 我们为预算了解的几次学习任务制定了元学习策略，该任务共同学习了基于图形卷积网络（GCN）的新型数据选择策略和一个基于示例的少数弹出分类器。我们的选择策略通过图形消息传递计算每个未标记数据的上下文敏感表示，然后将其用于预测顺序选择的信息性评分。我们通过在迷你象征，分层 - 象征和omniglot数据集上进行广泛的实验来验证我们的方法。结果表明，我们的几个学习策略的表现优于基准，这证明了我们方法的功效。
This paper tackles the problem of few-shot learning, which aims to learn new visual concepts from a few examples. A common problem setting in few-shot classification assumes random sampling strategy in acquiring data labels, which is inefficient in practical applications. In this work, we introduce a new budget-aware few-shot learning problem that not only aims to learn novel object categories, but also needs to select informative examples to annotate in order to achieve data efficiency. We develop a meta-learning strategy for our budget-aware few-shot learning task, which jointly learns a novel data selection policy based on a Graph Convolutional Network (GCN) and an example-based few-shot classifier. Our selection policy computes a context-sensitive representation for each unlabeled data by graph message passing, which is then used to predict an informativeness score for sequential selection. We validate our method by extensive experiments on the mini-ImageNet, tiered-ImageNet and Omniglot datasets. The results show our few-shot learning strategy outperforms baselines by a sizable margin, which demonstrates the efficacy of our method.
