Previous contrastive deep clustering methods mostly focus on instance-level information while overlooking the member relationship within groups/clusters, which may significantly undermine their representation learning and clustering capability. Recently, some group-contrastive methods have been developed, which, however, typically rely on the samples of the entire dataset to obtain pseudo labels and lack the ability to efficiently update the group assignments in a batch-wise manner. To tackle these critical issues, we present a novel end-to-end deep clustering framework with dynamic grouping and prototype aggregation, termed as DigPro. Specifically, the proposed dynamic grouping extends contrastive learning from instance-level to group-level, which is effective and efficient for timely updating groups. Meanwhile, we perform contrastive learning on prototypes in a spherical feature space, termed as prototype aggregation, which aims to maximize the inter-cluster distance. Notably, with an expectation-maximization framework, DigPro simultaneously takes advantage of compact intra-cluster connections, well-separated clusters, and efficient group updating during the self-supervised training. Extensive experiments on six image benchmarks demonstrate the superior performance of our approach over the state-of-the-art. Code is available at https://github.com/Regan-Zhang/DigPro.

翻译：以往的对比式深度聚类方法主要关注实例级信息，却忽略了组内/簇内成员关系，这可能严重削弱其表示学习与聚类能力。近来虽出现了一些组对比方法，但这些方法通常依赖整个数据集样本来获取伪标签，且缺乏以批次方式高效更新组分配的能力。为解决这些关键问题，我们提出了一种新颖的端到端深度聚类框架——动态分组与原型聚合（DigPro）。具体而言，所提出的动态分组将对比学习从实例级扩展至组级，能够高效及时地更新分组；同时，我们在球形特征空间中对原型执行对比学习（称为原型聚合），旨在最大化簇间距离。值得注意的是，借助期望最大化框架，DigPro可在自监督训练过程中同时兼顾紧凑的簇内连接、分离良好的簇以及高效的组更新。在六个图像基准数据集上的大量实验表明，本方法性能优于现有最先进技术。代码见 https://github.com/Regan-Zhang/DigPro。