Sequential Graph Convolutional Network for Active Learning

Abstract

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-LearningPreprint. Under review.