Deep Clustering

Benchmarks archive 2025-07-28

5 leaderboard tables shown for this task, 5 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

3 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 135 papers with code (307 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Deep Clustering for Unsupervised Learning of Visual Features
  15 Jul 2018 9 repositories listed Syntology ran 5 of 7 samples · 2 unverified · 4 pointer-only (licence)
  In this work, we present DeepCluster, a clustering method that jointly learns the parameters of a neural network and the cluster assignments of the resulting features.
• Deep clustering: Discriminative embeddings for segmentation and separation
  18 Aug 2015 8 repositories listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  The framework can be used without class labels, and therefore has the potential to be trained on a diverse set of sound types, and to generalize to novel sources.
• N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding
  16 Aug 2019 5 repositories listed Syntology ran 2 of 4 samples · 2 unverified · 2 pointer-only (licence)
  We study a number of local and global manifold learning methods on both the raw data and autoencoded embedding, concluding that UMAP in our framework is best able to find the most clusterable manifold in the embedding,…
• Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks
  18 Mar 2017 3 repositories listed Syntology ran 1 of 3 samples · 2 unverified · 3 pointer-only (licence)
  We evaluated uPIT on the WSJ0 and Danish two- and three-talker mixed-speech separation tasks and found that uPIT outperforms techniques based on Non-negative Matrix Factorization (NMF) and Computational Auditory Scene…
• Deep Temporal Graph Clustering
  18 May 2023 2 repositories listed
  To solve the problem, we propose a general framework for deep Temporal Graph Clustering called TGC, which introduces deep clustering techniques to suit the interaction sequence-based batch-processing pattern of temporal…
• Twin Contrastive Learning for Online Clustering
  21 Oct 2022 2 repositories listed
  Specifically, we find that when the data is projected into a feature space with a dimensionality of the target cluster number, the rows and columns of its feature matrix correspond to the instance and cluster…
• Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types
  21 Dec 2021 2 repositories listed
  We define a distance function between images, each of which is represented as a bag of embeddings, by the Euclidean distance between weighted averaged embeddings.
• Multi-Class Cell Detection Using Spatial Context Representation
  10 Oct 2021 2 repositories listed Syntology ran 1 of 2 samples · 1 unverified
  In digital pathology, both detection and classification of cells are important for automatic diagnostic and prognostic tasks.
• Deep Embedded K-Means Clustering
  30 Sep 2021 2 repositories listed
  To this end, we discard the decoder and propose a greedy method to optimize the representation.
• Attention-driven Graph Clustering Network
  12 Aug 2021 2 repositories listed
  The combination of the traditional convolutional network (i.
• Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering
  8 Dec 2020 2 repositories listed
  To address this issue, we are motivated by a UDA assumption of structural similarity across domains, and propose to directly uncover the intrinsic target discrimination via constrained clustering, where we constrain the…
• Dissimilarity Mixture Autoencoder for Deep Clustering
  15 Jun 2020 2 repositories listed
  The dissimilarity mixture autoencoder (DMAE) is a neural network model for feature-based clustering that incorporates a flexible dissimilarity function and can be integrated into any kind of deep learning architecture.
• Simple, Scalable, and Stable Variational Deep Clustering
  16 May 2020 2 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  We then choose to focus on variational deep clustering (VDC) methods, since they mostly meet those criteria except for simplicity, scalability, and stability.
• Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering
  19 Mar 2020 2 repositories listed Syntology ran 2 of 6 samples · 4 unverified
  To alleviate this risk, we are motivated by the assumption of structural domain similarity, and propose to directly uncover the intrinsic target discrimination via discriminative clustering of target data.
• Structural Deep Clustering Network
  5 Feb 2020 2 repositories listed Syntology ran 3 of 4 samples · 1 unverified
  The strength of deep clustering methods is to extract the useful representations from the data itself, rather than the structure of data, which receives scarce attention in representation learning.
• Leveraging tensor kernels to reduce objective function mismatch in deep clustering
  20 Jan 2020 2 repositories listed
  In this work we study OFM in deep clustering, and find that the popular autoencoder-based approach to deep clustering can lead to both reduced clustering performance, and a significant amount of OFM between the…
• DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps
  3 Oct 2019 2 repositories listed
  We show that DPSOM achieves superior clustering performance compared to current deep clustering methods on MNIST/Fashion-MNIST, while maintaining the favourable visualization properties of SOMs.
• Single-Channel Multi-Speaker Separation using Deep Clustering
  7 Jul 2016 2 repositories listed
  In this paper we extend the baseline system with an end-to-end signal approximation objective that greatly improves performance on a challenging speech separation.
• Tri-Learn Graph Fusion Network for Attributed Graph Clustering
  18 Jul 2025 1 repository listed
  To address these challenges, this study proposes a novel deep clustering framework that comprising GCN, Autoencoder (AE), and Graph Transformer, termed the Tri-Learn Graph Fusion Network (Tri-GFN).
• DeepNuParc: A Novel Deep Clustering Framework for Fine-scale Parcellation of Brain Nuclei Using Diffusion MRI Tractography
  10 Mar 2025 1 repository listed
  Diffusion MRI tractography is an advanced imaging technique that can estimate the brain's white matter structural connectivity to potentially reveal the topography of the nuclei of interest for studying its subdivisions.
• CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning
  2 Feb 2025 1 repository listed
  Auscultation plays a pivotal role in early respiratory and pulmonary disease diagnosis.
• Deep Clustering via Probabilistic Ratio-Cut Optimization
  1 Feb 2025 1 repository listed
  We propose a novel approach for optimizing the graph ratio-cut by modeling the binary assignments as random variables.
• Breaking the Reclustering Barrier in Centroid-based Deep Clustering
  4 Nov 2024 1 repository listed Syntology ran 0 of 5 samples · 5 unverified
  This work investigates an important phenomenon in centroid-based deep clustering (DC) algorithms: Performance quickly saturates after a period of rapid early gains.
• Robust Variational Contrastive Learning for Partially View-unaligned Clustering
  28 Oct 2024 1 repository listed
  To address these problems, we propose a robust framework, dubbed VariatIonal ConTrAstive Learning (VITAL), designed to learn both common and specific information simultaneously.
• Dying Clusters Is All You Need -- Deep Clustering With an Unknown Number of Clusters
  12 Oct 2024 1 repository listed
  We demonstrate the applicability of our approach by combining UNSEEN with the popular deep clustering algorithms DCN, DEC, and DKM and verify its effectiveness through an extensive experimental evaluation on several…
• Dual-Level Cross-Modal Contrastive Clustering
  6 Sep 2024 1 repository listed
  However, there is a gap between visual representation learning and textual semantic learning, and how to properly utilize the representation of two different modalities for clustering is still a big challenge.
• scASDC: Attention Enhanced Structural Deep Clustering for Single-cell RNA-seq Data
  9 Aug 2024 1 repository listed
  Single-cell RNA sequencing (scRNA-seq) data analysis is pivotal for understanding cellular heterogeneity.
• Image Clustering Algorithm Based on Self-Supervised Pretrained Models and Latent Feature Distribution Optimization
  4 Aug 2024 1 repository listed
  (2) In the latent feature space, by searching for k-nearest neighbor images for each training sample and shortening the distance between the training sample and its nearest neighbor, the discriminative power of latent…
• Contextual unsupervised deep clustering in digital pathology
  24 Jul 2024 1 repository listed
  Clustering can be used in medical imaging research to identify different domains within a specific dataset, aiding in a better understanding of subgroups or strata that may not have been annotated.
• Deep Online Probability Aggregation Clustering
  7 Jul 2024 1 repository listed Syntology ran 5 of 10 samples · 5 unverified · 10 pointer-only (licence)
  Combining machine clustering with deep models has shown remarkable superiority in deep clustering.

Syntology lines on 10 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.