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Sequential Graph Convolutional Network for Active Learning
2021
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
We propose a novel pool-based Active Learning framework constructed on a sequential Graph Convolution Network (GCN). Each images 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,
doi:10.1109/cvpr46437.2021.00946
fatcat:r65cu6zx4bgsphoz3k7knefnl4