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Image Clustering with Contrastive Learning and Multi-scale Graph Convolutional Networks
Deep clustering has recently attracted significant attention. Despite the
remarkable progress, most of the previous deep clustering works still suffer
from two limitations. First, many of them focus on some distribution-based
clustering loss, lacking the ability to exploit sample-wise (or
augmentation-wise) relationships via contrastive learning. Second, they often
neglect the indirect sample-wise structure information, overlooking the rich
possibilities of multi-scale neighborhood structure learning. In view of 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 neighborhood structure learning for the image clustering task. The
proposed IcicleGCN 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, with two random augmentations
performed on each image, the backbone network with two weight-sharing views is
utilized to learn the representations for the augmented samples, which are then
fed to ISM and JC-SLIM for instance-level and cluster-level contrastive
learning, respectively. Further, to enforce multi-scale neighborhood structure
learning, two streams of GCNs and an auto-encoder are simultaneously trained
via (i) the layer-wise interaction with representation fusion and (ii) the
joint self-adaptive learning that ensures their last-layer output distributions
to be consistent. Experiments on multiple image datasets demonstrate the
superior clustering performance of IcicleGCN over the state-of-the-art
Reinforcement Graph Clustering with Unknown Cluster Number
Deep graph clustering, which aims to group nodes into disjoint clusters by
neural networks in an unsupervised manner, has attracted great attention in
recent years. Although the performance has been largely improved, the excellent
performance of the existing methods heavily relies on an accurately predefined
cluster number, which is not always available in the real-world scenario. To
enable the deep graph clustering algorithms to work without the guidance of the
predefined cluster number, we propose a new deep graph clustering method termed
Reinforcement Graph Clustering (RGC). In our proposed method, cluster number
determination and unsupervised representation learning are unified into a
uniform framework by the reinforcement learning mechanism. Concretely, the
discriminative node representations are first learned with the contrastive
pretext task. Then, to capture the clustering state accurately with both local
and global information in the graph, both node and cluster states are
considered. Subsequently, at each state, the qualities of different cluster
numbers are evaluated by the quality network, and the greedy action is executed
to determine the cluster number. In order to conduct feedback actions, the
clustering-oriented reward function is proposed to enhance the cohesion of the
same clusters and separate the different clusters. Extensive experiments
demonstrate the effectiveness and efficiency of our proposed method. The source
code of RGC is shared at https://github.com/yueliu1999/RGC and a collection
(papers, codes and, datasets) of deep graph clustering is shared at
https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering on Github
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