# DeepDPM: Deep Clustering With an Unknown Number of Clusters

arXiv: https://arxiv.org/abs/2203.14309 · HTML: https://mlsanity.com/paper/2203.14309/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 76 references that are in the map.
- First posted on arXiv 27 Mar 2022.

## Builds on (most cited first, 17 of 17)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [SCAN: Learning to Classify Images without Labels](https://mlsanity.com/paper/2005.12320/) (2020)
- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)
- [Graph Embedded Pose Clustering for Anomaly Detection](https://mlsanity.com/paper/1912.11850/) (2019)
- [N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding](https://mlsanity.com/paper/1908.05968/) (2019)
- [Deep Continuous Clustering](https://mlsanity.com/paper/1803.01449/) (2018)
- [Video Face Clustering with Unknown Number of Clusters](https://mlsanity.com/paper/1908.03381/) (2019)
- [Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures](https://mlsanity.com/paper/1603.04868/) (2016)
- [Deep Learning with Nonparametric Clustering](https://mlsanity.com/paper/1501.03084/) (2015)
- [Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data](https://mlsanity.com/paper/2202.13312/) (2022)
- [Small-Variance Nonparametric Clustering on the Hypersphere](https://mlsanity.com/paper/1607.06407/) (2016)
- [Common Failure Modes of Subcluster-based Sampling in Dirichlet Process Gaussian Mixture Models -- and a Deep-learning Solution](https://mlsanity.com/paper/2203.13661/) (2022)

## Built on by (most cited first, 30 of 52)

- [Wavelet Convolutions for Large Receptive Fields](https://mlsanity.com/paper/2407.05848/) (2024)
- [Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery](https://mlsanity.com/paper/2305.06144/) (2023)
- [Visual Recognition with Deep Nearest Centroids](https://mlsanity.com/paper/2209.07383/) (2022)
- [Proxy Anchor-based Unsupervised Learning for Continuous Generalized Category Discovery](https://mlsanity.com/paper/2307.10943/) (2023)
- [Reinforcement Graph Clustering with Unknown Cluster Number](https://mlsanity.com/paper/2308.06827/) (2023)
- [Open-world Semi-supervised Novel Class Discovery](https://mlsanity.com/paper/2305.13095/) (2023)
- [Learning Clustering-based Prototypes for Compositional Zero-shot Learning](https://mlsanity.com/paper/2502.06501/) (2025)
- [P2OT: Progressive Partial Optimal Transport for Deep Imbalanced Clustering](https://mlsanity.com/paper/2401.09266/) (2024)
- [Neural Clustering based Visual Representation Learning](https://mlsanity.com/paper/2403.17409/) (2024)
- [e-CLIP: Large-Scale Vision-Language Representation Learning in E-commerce](https://mlsanity.com/paper/2207.00208/) (2022)
- [End-to-end Learnable Clustering for Intent Learning in Recommendation](https://mlsanity.com/paper/2401.05975/) (2024)
- [What's in a Name? Beyond Class Indices for Image Recognition](https://mlsanity.com/paper/2304.02364/) (2023)
- [BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction](https://mlsanity.com/paper/2502.14676/) (2025)
- [Unsupervised Video Highlight Detection by Learning from Audio and Visual Recurrence](https://mlsanity.com/paper/2407.13933/) (2024)
- [Generalised Mutual Information for Discriminative Clustering](https://mlsanity.com/paper/2210.06300/) (2022)
- [An Empirical Study into Clustering of Unseen Datasets with Self-Supervised Encoders](https://mlsanity.com/paper/2406.02465/) (2024)
- [cDP-MIL: Robust Multiple Instance Learning via Cascaded Dirichlet Process](https://mlsanity.com/paper/2407.11448/) (2024)
- [PRAGA: Prototype-aware Graph Adaptive Aggregation for Spatial Multi-modal Omics Analysis](https://mlsanity.com/paper/2409.12728/) (2024)
- [Riemannian Optimization on Relaxed Indicator Matrix Manifold](https://mlsanity.com/paper/2503.20505/) (2025)
- [ALPS: An Auto-Labeling and Pre-training Scheme for Remote Sensing Segmentation With Segment Anything Model](https://mlsanity.com/paper/2406.10855/) (2024)
- [Dying Clusters Is All You Need -- Deep Clustering With an Unknown Number of Clusters](https://mlsanity.com/paper/2410.09491/) (2024)
- [PromptORE -- A Novel Approach Towards Fully Unsupervised Relation Extraction](https://mlsanity.com/paper/2304.01209/) (2023)
- [Clustering via Self-Supervised Diffusion](https://mlsanity.com/paper/2507.04283/) (2025)
- [TDEC: Deep Embedded Image Clustering with Transformer and Distribution Information](https://mlsanity.com/paper/2603.26746/) (2026)
- [Deep Embedding Clustering Driven by Sample Stability](https://mlsanity.com/paper/2401.15989/) (2024)
- [SP2OT: Semantic-Regularized Progressive Partial Optimal Transport for Imbalanced Clustering](https://mlsanity.com/paper/2404.03446/) (2024)
- [Deep Online Probability Aggregation Clustering](https://mlsanity.com/paper/2407.05246/) (2024)
- [Deep Clustering with Associative Memories](https://mlsanity.com/paper/2601.00963/) (2026)
- [DIVA: A Dirichlet Process Mixtures Based Incremental Deep Clustering Algorithm via Variational Auto-Encoder](https://mlsanity.com/paper/2305.14067/) (2023)
- [Generalised Mutual Information: a Framework for Discriminative Clustering](https://mlsanity.com/paper/2309.02858/) (2023)

## Abstract (arXiv)

Deep Learning (DL) has shown great promise in the unsupervised task of clustering. That said, while in classical (i.e., non-deep) clustering the benefits of the nonparametric approach are well known, most deep-clustering methods are parametric: namely, they require a predefined and fixed number of clusters, denoted by K. When K is unknown, however, using model-selection criteria to choose its optimal value might become computationally expensive, especially in DL as the training process would have to be repeated numerous times. In this work, we bridge this gap by introducing an effective deep-clustering method that does not require knowing the value of K as it infers it during the learning. Using a split/merge framework, a dynamic architecture that adapts to the changing K, and a novel loss, our proposed method outperforms existing nonparametric methods (both classical and deep ones). While the very few existing deep nonparametric methods lack scalability, we demonstrate ours by being the first to report the performance of such a method on ImageNet. We also demonstrate the importance of inferring K by showing how methods that fix it deteriorate in performance when their assumed K value gets further from the ground-truth one, especially on imbalanced datasets. Our code is available at https://github.com/BGU-CS-VIL/DeepDPM.