We present Consistent Assignment of Views over Random Partitions (CARP), a
self-supervised clustering method for representation learning of visual
features. CARP learns prototypes in an end-to-end online fashion using gradient
descent without additional non-differentiable modules to solve the cluster
assignment problem. CARP optimizes a new pretext task based on random
partitions of prototypes that regularizes the model and enforces consistency
between views' assignments. Additionally, our method improves training
stability and prevents collapsed solutions in joint-embedding training. Through
an extensive evaluation, we demonstrate that CARP's representations are
suitable for learning downstream tasks. We evaluate CARP's representations
capabilities in 17 datasets across many standard protocols, including linear
evaluation, few-shot classification, k-NN, k-means, image retrieval, and copy
detection. We compare CARP performance to 11 existing self-supervised methods.
We extensively ablate our method and demonstrate that our proposed random
partition pretext task improves the quality of the learned representations by
devising multiple random classification tasks. In transfer learning tasks, CARP
achieves the best performance on average against many SSL methods trained for a
longer time.

CARP 是一种无监督聚类方法，用于学习视觉特征的表示学习。CARP 通过随机划分原型进行端到端在线学习，使用梯度下降优化新的预训练任务，提高模型的一致性和稳定性，并证明其在多个下游任务上的表现优于其他自监督方法。

通过一致性分配随机分区的视图进行表示学习

Representation Learning via Consistent Assignment of Views over Random  Partitions

Multi-view attributed graph clustering is an important approach to partition
multi-view data based on the attribute feature and adjacent matrices from
different views. Some attempts have been made in utilizing Graph Neural Network
(GNN), which have achieved promising clustering performance. Despite this, few
of them pay attention to the inherent specific information embedded in multiple
views. Meanwhile, they are incapable of recovering the latent high-level
representation from the low-level ones, greatly limiting the downstream
clustering performance. To fill these gaps, a novel Dual Information enhanced
multi-view Attributed Graph Clustering (DIAGC) method is proposed in this
paper. Specifically, the proposed method introduces the Specific Information
Reconstruction (SIR) module to disentangle the explorations of the consensus
and specific information from multiple views, which enables GCN to capture the
more essential low-level representations. Besides, the Mutual Information
Maximization (MIM) module maximizes the agreement between the latent high-level
representation and low-level ones, and enables the high-level representation to
satisfy the desired clustering structure with the help of the Self-supervised
Clustering (SC) module. Extensive experiments on several real-world benchmarks
demonstrate the effectiveness of the proposed DIAGC method compared with the
state-of-the-art baselines.

提出了一种名为 DIAGC 的新型多视图图聚类方法，引入了特定信息重构、互信息最大化和自监督聚类模块来增强图神经网络的能力，实现对多视图数据进行更精确的聚类。