  Face clustering has attracted rising research interest recently to take
advantage of massive amounts of face images on the web. State-of-the-art
performance has been achieved by Graph Convolutional Networks (GCN) due to
their powerful representation capacity. However, existing GCN-based methods
build face graphs mainly according to kNN relations in the feature space, which
may lead to a lot of noise edges connecting two faces of different classes. The
face features will be polluted when messages pass along these noise edges, thus
degrading the performance of GCNs. In this paper, a novel algorithm named
Ada-NETS is proposed to cluster faces by constructing clean graphs for GCNs. In
Ada-NETS, each face is transformed to a new structure space, obtaining robust
features by considering face features of the neighbour images. Then, an
adaptive neighbour discovery strategy is proposed to determine a proper number
of edges connecting to each face image. It significantly reduces the noise
edges while maintaining the good ones to build a graph with clean yet rich
edges for GCNs to cluster faces. Experiments on multiple public clustering
datasets show that Ada-NETS significantly outperforms current state-of-the-art
methods, proving its superiority and generalization. Code is available at
https://github.com/damo-cv/Ada-NETS.
