GCL-OSDA: Uncertainty prediction-based graph collaborative learning for open-set domain adaptation

Abstract

Open-set domain adaptation (OSDA), which allows the target domain to store invisible class samples in the source domain, has recently received significant attention. In this paper, we propose a new unsupervised OSDA classification framework using an evidential network and multi-binary classifier and consider their jointly selected samples as a pseudo-labelled sample set of an unknown class. Specifically, this study designed an evidential network based on the D-S evidence theory to predict the degree of belief that a sample belongs to an unknown class. By selecting samples with high-uncertainty, false positive samples can be removed, which improves the reliability of unknown sample selection. Then, to better explore the intra-class relationship, an open-set graph convolutional network (OSGC) is proposed to extract distinguishable features of known and unknown samples in a weighted adversarial adaptation manner. Moreover, this paper presents a graph collaborative learning strategy to retrain the unknown recognition module (URM) with high confidence pseudo-labelled samples, which is predicted by the graph convolution network (GCN), where the target known class distribution is learned. Experimental results show that the proposed method outperforms state-of-the-art OSDA algorithms on three benchmark datasets and maintains a high recognition accuracy for unknown classes over a wide range of openness.