Contemporary deep clustering approaches often rely on either contrastive or
non-contrastive techniques to acquire effective representations for clustering
tasks. Contrastive methods leverage negative pairs to achieve homogenous
representations but can introduce class collision issues, potentially
compromising clustering performance. On the contrary, non-contrastive
techniques prevent class collisions but may produce non-uniform representations
that lead to clustering collapse. In this work, we propose a novel end-to-end
deep clustering approach named PIPCDR, designed to harness the strengths of
both approaches while mitigating their limitations. PIPCDR incorporates a
positive instance proximity loss and a cluster dispersion regularizer. The
positive instance proximity loss ensures alignment between augmented views of
instances and their sampled neighbors, enhancing within-cluster compactness by
selecting genuinely positive pairs within the embedding space. Meanwhile, the
cluster dispersion regularizer maximizes inter-cluster distances while
minimizing within-cluster compactness, promoting uniformity in the learned
representations. PIPCDR excels in producing well-separated clusters, generating
uniform representations, avoiding class collision issues, and enhancing
within-cluster compactness. We extensively validate the effectiveness of PIPCDR
within an end-to-end Majorize-Minimization framework, demonstrating its
competitive performance on moderate-scale clustering benchmark datasets and
establishing new state-of-the-art results on large-scale datasets.

本研究提出了一种名为 PIPCDR 的全新端到端深度聚类方法，它同时利用对比方法和非对比技术的优势，解决了类碰撞问题和聚类崩溃问题，通过正样本邻近损失和聚类分散正则化器实现了生成良好分离聚类和均匀表示的目标，并在大规模数据集上取得了最新的技术成果。

利用正向相似度和聚类离散度学习提升聚类表示

Enhancing Clustering Representations with Positive Proximity and Cluster  Dispersion Learning

This paper presents a neural network-based end-to-end clustering framework.
We design a novel strategy to utilize the contrastive criteria for pushing
data-forming clusters directly from raw data, in addition to learning a feature
embedding suitable for such clustering. The network is trained with weak
labels, specifically partial pairwise relationships between data instances. The
cluster assignments and their probabilities are then obtained at the output
layer by feed-forwarding the data. The framework has the interesting
characteristic that no cluster centers need to be explicitly specified, thus
the resulting cluster distribution is purely data-driven and no distance
metrics need to be predefined. The experiments show that the proposed approach
beats the conventional two-stage method (feature embedding with k-means) by a
significant margin. It also compares favorably to the performance of the
standard cross entropy loss for classification. Robustness analysis also shows
that the method is largely insensitive to the number of clusters. Specifically,
we show that the number of dominant clusters is close to the true number of
clusters even when a large k is used for clustering.

本文介绍了一种基于神经网络的端到端聚类框架，该框架可从原始数据中推动形成聚类，且无需提前定义聚类中心，具有较好的性能且对聚类数量不敏感。