# You Can Trust Your Clustering Model: A Parameter-free Self-Boosting Plug-in for Deep Clustering arXiv: https://arxiv.org/abs/2511.21193 · HTML: https://mlsanity.com/paper/2511.21193/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 26 of its 42 references that are in the map. - First posted on arXiv 26 Nov 2025. ## Builds on (most cited first, 26 of 26) - [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020) - [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019) - [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015) - [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020) - [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020) - [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020) - [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015) - [Prototypical Contrastive Learning of Unsupervised Representations](https://mlsanity.com/paper/2005.04966/) (2020) - [SCAN: Learning to Classify Images without Labels](https://mlsanity.com/paper/2005.12320/) (2020) - [Contrastive Clustering](https://mlsanity.com/paper/2009.09687/) (2020) - [SPICE: Semantic Pseudo-labeling for Image Clustering](https://mlsanity.com/paper/2103.09382/) (2021) - [Multi-Objective Interpolation Training for Robustness to Label Noise](https://mlsanity.com/paper/2012.04462/) (2020) - [Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation](https://mlsanity.com/paper/2106.00131/) (2021) - [Graph Contrastive Clustering](https://mlsanity.com/paper/2104.01429/) (2021) - [Twin Contrastive Learning for Online Clustering](https://mlsanity.com/paper/2210.11680/) (2022) - [Learning Representation for Clustering via Prototype Scattering and Positive Sampling](https://mlsanity.com/paper/2111.11821/) (2021) - [MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering](https://mlsanity.com/paper/2105.01899/) (2021) - [Attention-driven Graph Clustering Network](https://mlsanity.com/paper/2108.05499/) (2021) - [You Never Cluster Alone](https://mlsanity.com/paper/2106.01908/) (2021) - [Image Clustering with External Guidance](https://mlsanity.com/paper/2310.11989/) (2023) - [Semantic-Enhanced Image Clustering](https://mlsanity.com/paper/2208.09849/) (2022) - [Stable Cluster Discrimination for Deep Clustering](https://mlsanity.com/paper/2311.14310/) (2023) - [Deep Attention-guided Graph Clustering with Dual Self-supervision](https://mlsanity.com/paper/2111.05548/) (2021) - [Contextually Affinitive Neighborhood Refinery for Deep Clustering](https://mlsanity.com/paper/2312.07806/) (2023) - [Towards Calibrated Deep Clustering Network](https://mlsanity.com/paper/2403.02998/) (2024) - [Deep Online Probability Aggregation Clustering](https://mlsanity.com/paper/2407.05246/) (2024) ## Built on by (most cited first, 2 of 2) - [CLUBench: A Clustering Benchmark](https://mlsanity.com/paper/2605.29933/) (2026) - [Language-Assisted Image Clustering Guided by Discriminative Relational Signals and Adaptive Semantic Centers](https://mlsanity.com/paper/2603.24275/) (2026) ## Abstract (arXiv) 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×. Code is available at .