We propose a novel generic sequential Graph Convolution Network (GCN) training for Active Learning. Each of the unlabelled and labelled examples is represented through a pre-trained learner as nodes of a graph and their similarities as edges. With the available few labelled examples as seed annotations, the parameters of the Graphs are optimised to minimise the binary cross-entropy loss to identify labelled vs unlabelled. Based on the confidence score of the nodes in the graph we sub-sample unlabelled examples to annotate where inherited uncertainties correlate. With the newly annotated examples along with the existing ones, the parameters of the graph are optimised to minimise the modified objective. We evaluated our method on four publicly available image classification benchmarks. Our method outperforms several competitive baselines and existing arts. The implementations of this paper can be found here: https://github.com/razvancaramalau/Sequential-GCN-for-Active-Learning

本文提出了一种基于图卷积网络（GCN）的新颖池化主动学习框架，利用GCN对节点进行消息传递操作识别出已标记和未标记的节点，并利用节点的嵌入和置信度得分以及CoreSet和基于不确定性的采样技术选择未标记示例，反转其标签并重新训练学习者以优化下游任务，并评估了该方法在多个应用上的性能，实验结果表明，该方法在图像分类和手势估计等领域超越多个基线模型达到了新的最优性能。

基于序列图卷积网络的主动学习