In this paper, we present a novel deep image clustering approach termed PICI, which enforces the partial information discrimination and the cross-level interaction in a joint learning framework. In particular, we leverage a Transformer encoder as the backbone, through which the masked image modeling with two paralleled augmented views is formulated. After deriving the class tokens from the masked images by the Transformer encoder, three partial information learning modules are further incorporated, including the PISD module for training the auto-encoder via masked image reconstruction, the PICD module for employing two levels of contrastive learning, and the CLI module for mutual interaction between the instance-level and cluster-level subspaces. Extensive experiments have been conducted on six real-world image datasets, which demononstrate the superior clustering performance of the proposed PICI approach over the state-of-the-art deep clustering approaches. The source code is available at https://github.com/Regan-Zhang/PICI.

本文介绍了一种名为PICI的新型深度图像聚类方法，通过部分信息辨别和跨层级交互在联合学习框架中进行。通过使用Transformer编码器作为骨干网络，我们形成了具有两个并行增广视图的蒙版图像建模。从通过Transformer编码器从蒙版图像中导出的类别标记开始，我们进一步融入了三个部分信息学习模块，包括通过蒙版图像重构训练自动编码器的PISD模块、使用两个层次的对比学习的PICD模块，以及相互交互的实例级和群集级子空间之间的CLI模块。我们在六个真实世界的图像数据集上进行了大量实验，结果表明所提出的PICI方法在深度聚类方法方面具有优越的聚类性能。源代码可在此https URL找到。

通过部分信息辨识和跨层交互学习聚类表示