  Deep clustering has shown its promising capability in joint representation
learning and clustering via deep neural networks. Despite the significant
progress, the existing deep clustering works mostly utilize some
distribution-based clustering loss, lacking the ability to unify representation
learning and multi-scale structure learning. To address this, this paper
presents a new deep clustering approach termed image clustering with
contrastive learning and multi-scale graph convolutional networks (IcicleGCN),
which bridges the gap between convolutional neural network (CNN) and graph
convolutional network (GCN) as well as the gap between contrastive learning and
multi-scale structure learning for the deep clustering task. Our framework
consists of four main modules, namely, the CNN-based backbone, the Instance
Similarity Module (ISM), the Joint Cluster Structure Learning and Instance
reconstruction Module (JC-SLIM), and the Multi-scale GCN module (M-GCN).
Specifically, the backbone network with two weight-sharing views is utilized to
learn the representations for the two augmented samples (from each image). The
learned representations are then fed to ISM and JC-SLIM for joint
instance-level and cluster-level contrastive learning, respectively, during
which an auto-encoder in JC-SLIM is also pretrained to serve as a bridge to the
M-GCN module. Further, to enforce multi-scale neighborhood structure learning,
two streams of GCNs and the auto-encoder are simultaneously trained via (i) the
layer-wise interaction with representation fusion and (ii) the joint
self-adaptive learning. Experiments on multiple image datasets demonstrate the
superior clustering performance of IcicleGCN over the state-of-the-art. The
code is available at https://github.com/xuyuankun631/IcicleGCN.
