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Diffusion has shown great success in improving accuracy of unsupervised imageretrieval systems by utilizing high-order structures of image manifold.However, existing diffusion methods suffer from three major limitations: 1)they usually rely on local structures without considering global manifoldinformation; 2) they focus on improving pair-wise similarities within existingimages input output transductively while lacking flexibility to learnrepresentations for novel unseen instances inductively; 3) they fail to scaleto large datasets due to prohibitive memory consumption and computationalburden due to intrinsic high-order operations on the whole graph. In thispaper, to address these limitations, we propose a novel method, Graph DiffusionNetworks (GRAD-Net), that adopts graph neural networks (GNNs), a novel variantof deep learning algorithms on irregular graphs. GRAD-Net learns semanticrepresentations by exploiting both local and global structures of imagemanifold in an unsupervised fashion. By utilizing sparse coding techniques,GRAD-Net not only preserves global information on the image manifold, but alsoenables scalable training and efficient querying. Experiments on several largebenchmark datasets demonstrate effectiveness of our method overstate-of-the-art diffusion algorithms for unsupervised image retrieval.

Learning Global and Local Consistent Representations for Unsupervised Image Retrieval via Deep Graph Diffusion Networks