You Can Trust Your Clustering Model: A Parameter-free Self-Boosting Plug-in for Deep Clustering

TL;DR

DCBoost is a parameter-free plug-in that enhances global feature structures in deep clustering models by leveraging local structural cues, leading to significant improvements in clustering accuracy and cluster separation.

Contribution

It introduces a novel, parameter-free self-boosting method that uses adaptive neighbor filtering and discriminative loss to improve deep clustering performance.

Findings

Improves state-of-the-art clustering performance by over 3%.

Increases silhouette coefficient by more than 7 times.

Effectively enhances global feature separability in deep clustering models.

Abstract

Recent deep clustering models have produced impressive clustering performance. However, a common issue with existing methods is the disparity between global and local feature structures. While local structures typically show strong consistency and compactness within class samples, global features often present intertwined boundaries and poorly separated clusters. Motivated by this observation, we propose DCBoost, a parameter-free plug-in designed to enhance the global feature structures of current deep clustering models. By harnessing reliable local structural cues, our method aims to elevate clustering performance effectively. Specifically, we first identify high-confidence samples through adaptive $k$-nearest neighbors-based consistency filtering, aiming to select a sufficient number of samples with high label reliability to serve as trustworthy anchors for self-supervision. Subsequently, these samples are utilized to compute a discriminative loss, which promotes both intra-class compactness and inter-class separability, to guide network optimization. Extensive experiments across various benchmark datasets showcase that our DCBoost significantly improves the clustering performance of diverse existing deep clustering models. Notably, our method improves the performance of current state-of-the-art baselines (e.g., ProPos) by more than 3% and amplifies the silhouette coefficient by over $7\times$. Code is available at <https://github.com/l-h-y168/DCBoost>.