Unsupervised Image Classification

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

7 leaderboard tables shown for this task, 7 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

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

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 30 papers with code (45 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.

• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
  12 Jun 2016 38 repositories listed Syntology ran 5 of 6 samples · 1 unverified
  This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner.
• Adversarial Autoencoders
  18 Nov 2015 29 repositories listed Syntology ran 8 of 12 samples · 4 unverified · 9 pointer-only (licence)
  In this paper, we propose the "adversarial autoencoder" (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the…
• 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.
• 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.
• Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
  19 Nov 2015 5 repositories listed
  Our approach is based on an objective function that trades-off mutual information between observed examples and their predicted categorical class distribution, against robustness of the classifier to an adversarial…
• iBOT: Image BERT Pre-Training with Online Tokenizer
  15 Nov 2021 2 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  We present a self-supervised framework iBOT that can perform masked prediction with an online tokenizer.
• Self-Supervised Learning by Estimating Twin Class Distributions
  14 Oct 2021 2 repositories listed Syntology ran 5 of 15 samples · 10 unverified
  To solve this problem, we propose to maximize the mutual information between the input and the class predictions.
• Self-Supervised Classification Network
  19 Mar 2021 2 repositories listed
  To guarantee non-degenerate solutions (i.
• Unsupervised Feature Learning by Cross-Level Instance-Group Discrimination
  9 Aug 2020 2 repositories listed Syntology ran 2 of 8 samples · 6 unverified
  Unsupervised feature learning has made great strides with contrastive learning based on instance discrimination and invariant mapping, as benchmarked on curated class-balanced datasets.
• 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.
• Inferencing Based on Unsupervised Learning of Disentangled Representations
  7 Mar 2018 2 repositories listed
  Combining Generative Adversarial Networks (GANs) with encoders that learn to encode data points has shown promising results in learning data representations in an unsupervised way.
• Learning Discrete Representations via Information Maximizing Self-Augmented Training
  28 Feb 2017 2 repositories listed
  Learning discrete representations of data is a central machine learning task because of the compactness of the representations and ease of interpretation.
• 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.
• Let Go of Your Labels with Unsupervised Transfer
  11 Jun 2024 1 repository listed Syntology ran 3 of 4 samples · 1 unverified · 4 pointer-only (licence)
  In particular, TURTLE matches the average performance of CLIP zero-shot on 26 datasets by employing the same representation space, spanning a wide range of architectures and model sizes.
• IPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised Feature Representation
  18 Mar 2024 1 repository listed
  In this paper, we propose a novel self-supervised learning approach named Iterative Pseudo-supervised Contrastive Learning (IPCL), which utilizes a balanced combination of image augmentations and pseudo-class…
• The VampPrior Mixture Model
  6 Feb 2024 1 repository listed
  Current clustering priors for deep latent variable models (DLVMs) require defining the number of clusters a-priori and are susceptible to poor initializations.
• Improving Cross-domain Few-shot Classification with Multilayer Perceptron
  15 Dec 2023 1 repository listed
  Multilayer perceptron (MLP) has shown its capability to learn transferable representations in various downstream tasks, such as unsupervised image classification and supervised concept generalization.
• Stable Cluster Discrimination for Deep Clustering
  24 Nov 2023 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  Meanwhile, one-stage methods are developed mainly for representation learning rather than clustering, where various constraints for cluster assignments are designed to avoid collapsing explicitly.
• The Pursuit of Human Labeling: A New Perspective on Unsupervised Learning
  21 Sep 2023 1 repository listed
  Despite its simplicity, HUME outperforms a supervised linear classifier on top of self-supervised representations on the STL-10 dataset by a large margin and achieves comparable performance on the CIFAR-10 dataset.
• MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillation
  21 Mar 2023 1 repository listed
  We present a new method of self-supervised learning and knowledge distillation based on the multi-views and multi-representations (MV-MR).
• Capsule Network based Contrastive Learning of Unsupervised Visual Representations
  22 Sep 2022 1 repository listed
  Capsule Networks have shown tremendous advancement in the past decade, outperforming the traditional CNNs in various task due to it's equivariant properties.
• DeepDPM: Deep Clustering With an Unknown Number of Clusters
  27 Mar 2022 1 repository listed
  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).
• Unsupervised Visual Representation Learning by Online Constrained K-Means
  24 May 2021 1 repository listed Syntology ran 3 of 6 samples · 3 unverified
  Clustering is to assign each instance a pseudo label that will be used to learn representations in discrimination.
• Improving Unsupervised Image Clustering With Robust Learning
  21 Dec 2020 1 repository listed
  Unsupervised image clustering methods often introduce alternative objectives to indirectly train the model and are subject to faulty predictions and overconfident results.
• Improving Self-Organizing Maps with Unsupervised Feature Extraction
  4 Sep 2020 1 repository listed
  We conduct a comparative study on the SOM classification accuracy with unsupervised feature extraction using two different approaches: a machine learning approach with Sparse Convolutional Auto-Encoders using…
• Self-Supervised Learning for Large-Scale Unsupervised Image Clustering
  24 Aug 2020 1 repository listed Syntology ran 2 of 11 samples · 9 unverified
  Unsupervised learning has always been appealing to machine learning researchers and practitioners, allowing them to avoid an expensive and complicated process of labeling the data.
• Mitigating Embedding and Class Assignment Mismatch in Unsupervised Image Classification
  1 Aug 2020 1 repository listed
  Unsupervised image classification is a challenging computer vision task.
• Unsupervised Image Classification for Deep Representation Learning
  20 Jun 2020 1 repository listed
  Extensive experiments on ImageNet dataset have been conducted to prove the effectiveness of our method.
• Deep Transformation-Invariant Clustering
  19 Jun 2020 1 repository listed Syntology ran 1 of 10 samples · 9 unverified
  In contrast, we present an orthogonal approach that does not rely on abstract features but instead learns to predict image transformations and performs clustering directly in image space.
• PixelGAN Autoencoders
  2 Jun 2017 1 repository listed
  In this paper, we describe the "PixelGAN autoencoder", a generative autoencoder in which the generative path is a convolutional autoregressive neural network on pixels (PixelCNN) that is conditioned on a latent code,…

Syntology lines on 13 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.