Image Clustering

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

55 leaderboard tables shown for this task, 55 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. 10 shown of 55 until expanded.

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

44 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 44 until expanded.

Subtasks archive 2025-07-28

4 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 118 papers with code (236 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.

• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
  19 Nov 2015 258 repositories listed Syntology ran 113 of 219 samples · 106 unverified · 111 pointer-only (licence)
  In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications.
• Auto-Encoding Variational Bayes
  20 Dec 2013 144 repositories listed Syntology ran 112 of 199 samples · 87 unverified · 103 pointer-only (licence)
  First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods.
• Unsupervised Deep Embedding for Clustering Analysis
  19 Nov 2015 23 repositories listed Syntology ran 2 of 25 samples · 23 unverified · 3 pointer-only (licence)
  Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms.
• 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.
• Invariant Information Clustering for Unsupervised Image Classification and Segmentation
  17 Jul 2018 6 repositories listed Syntology ran 2 of 18 samples · 16 unverified
  The method is not specialised to computer vision and operates on any paired dataset samples; in our experiments we use random transforms to obtain a pair from each image.
• Self-labelling via simultaneous clustering and representation learning
  13 Nov 2019 5 repositories listed Syntology ran 7 of 18 samples · 11 unverified · 3 pointer-only (licence)
  Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks.
• 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,…
• Sparse Subspace Clustering: Algorithm, Theory, and Applications
  5 Mar 2012 4 repositories listed
  In this paper, we propose and study an algorithm, called Sparse Subspace Clustering (SSC), to cluster data points that lie in a union of low-dimensional subspaces.
• Learning Hierarchical Graph Neural Networks for Image Clustering
  3 Jul 2021 3 repositories listed
  Our hierarchical GNN uses a novel approach to merge connected components predicted at each level of the hierarchy to form a new graph at the next level.
• An Image Clustering Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids and MMD Distance
  10 Jun 2019 3 repositories listed
  The algorithm uses PEDCC (Predefined Evenly-Distributed Class Centroids) as the clustering centers, which ensures the inter-class distance of latent features is maximal, and adds data distribution constraint, data…
• Deep Subspace Clustering Networks
  8 Sep 2017 3 repositories listed
  We present a novel deep neural network architecture for unsupervised subspace clustering.
• Joint Unsupervised Learning of Deep Representations and Image Clusters
  13 Apr 2016 3 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  In this paper, we propose a recurrent framework for Joint Unsupervised LEarning (JULE) of deep representations and image clusters.
• MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations
  15 Feb 2024 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified
  We introduce MIM (Masked Image Modeling)-Refiner, a contrastive learning boost for pre-trained MIM models.
• 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.
• Contrastive Clustering
  21 Sep 2020 2 repositories listed Syntology ran 2 of 7 samples · 5 unverified
  In this paper, we propose a one-stage online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning.
• Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction
  15 Jun 2020 2 repositories listed Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
  To learn intrinsic low-dimensional structures from high-dimensional data that most discriminate between classes, we propose the principle of Maximal Coding Rate Reduction (MCR²), an information-theoretic measure that…
• 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.
• SCAN: Learning to Classify Images without Labels
  25 May 2020 2 repositories listed
  First, a self-supervised task from representation learning is employed to obtain semantically meaningful features.
• 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…
• CNN features are also great at unsupervised classification
  6 Jul 2017 2 repositories listed
  This paper aims at providing insight on the transferability of deep CNN features to unsupervised problems.
• Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders
  23 Mar 2017 2 repositories listed
  Traditional image clustering methods take a two-step approach, feature learning and clustering, sequentially.
• Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit
  5 Jul 2015 2 repositories listed
  Subspace clustering methods based on ℓ₁, ℓ₂ or nuclear norm regularization have become very popular due to their simplicity, theoretical guarantees and empirical success.
• Graph Degree Linkage: Agglomerative Clustering on a Directed Graph
  25 Aug 2012 2 repositories listed
  We explore the different roles of two fundamental concepts in graph theory, indegree and outdegree, in the context of clustering.
• Structural-Spectral Graph Convolution with Evidential Edge Learning for Hyperspectral Image Clustering
  11 Jun 2025 1 repository listed
  Hyperspectral image (HSI) clustering assigns similar pixels to the same class without any annotations, which is an important yet challenging task.
• 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.
• Graph Cut-guided Maximal Coding Rate Reduction for Learning Image Embedding and Clustering
  25 Dec 2024 1 repository listed
  In the era of pre-trained models, image clustering task is usually addressed by two relevant stages: a) to produce features from pre-trained vision models; and b) to find clusters from the pre-trained features.
• I Spy With My Little Eye: A Minimum Cost Multicut Investigation of Dataset Frames
  2 Dec 2024 1 repository listed
  For broad visual frames, DINOv2 is a suitable embedding space, while ConvNeXt V2 returns a larger number of clusters which contain fine-grain differences, i.
• 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.
• 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.

Syntology lines on 12 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.