  We propose a novel pool-based Active Learning framework constructed on a
sequential Graph Convolution Network (GCN). Each image's feature from a pool of
data represents a node in the graph and the edges encode their similarities.
With a small number of randomly sampled images as seed labelled examples, we
learn the parameters of the graph to distinguish labelled vs unlabelled nodes
by minimising the binary cross-entropy loss. GCN performs message-passing
operations between the nodes, and hence, induces similar representations of the
strongly associated nodes. We exploit these characteristics of GCN to select
the unlabelled examples which are sufficiently different from labelled ones. To
this end, we utilise the graph node embeddings and their confidence scores and
adapt sampling techniques such as CoreSet and uncertainty-based methods to
query the nodes. We flip the label of newly queried nodes from unlabelled to
labelled, re-train the learner to optimise the downstream task and the graph to
minimise its modified objective. We continue this process within a fixed
budget. We evaluate our method on 6 different benchmarks:4 real image
classification, 1 depth-based hand pose estimation and 1 synthetic RGB image
classification datasets. Our method outperforms several competitive baselines
such as VAAL, Learning Loss, CoreSet and attains the new state-of-the-art
performance on multiple applications The implementations can be found here:
https://github.com/razvancaramalau/Sequential-GCN-for-Active-Learning
