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Unsupervised Image Classification

Models that learn to label each image (i.e. cluster the dataset into its ground truth classes) without seeing the ground truth labels.

Image credit: ImageNet clustering results of SCAN: Learning to Classify Images without Labels (ECCV 2020)

Papers

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Showing 1–45 of 45 papers

TitleStatusHype
Breaking the Reclustering Barrier in Centroid-based Deep ClusteringCode1
Let Go of Your Labels with Unsupervised TransferCode2
IPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised Feature RepresentationCode0
The VampPrior Mixture ModelCode0
Improving Cross-domain Few-shot Classification with Multilayer PerceptronCode1
Stable Cluster Discrimination for Deep ClusteringCode1
The Pursuit of Human Labeling: A New Perspective on Unsupervised LearningCode1
Contrastive Knowledge Amalgamation for Unsupervised Image Classification—0
ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision—0
MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillationCode0
Minimalistic Unsupervised Learning with the Sparse Manifold Transform—0
Capsule Network based Contrastive Learning of Unsupervised Visual RepresentationsCode1
Loss Function Entropy Regularization for Diverse Decision Boundaries—0
LatentGAN Autoencoder: Learning Disentangled Latent Distribution—0
DeepDPM: Deep Clustering With an Unknown Number of ClustersCode2
Revisiting the Transferability of Supervised Pretraining: an MLP Perspective—0
iBOT: Image BERT Pre-Training with Online TokenizerCode1
Self-Supervised Learning by Estimating Twin Class DistributionsCode1
GUIDED MCMC FOR SPARSE BAYESIAN MODELS TO DETECT RARE EVENTS IN IMAGES SANS LABELED DATA—0
Unsupervised Visual Representation Learning by Online Constrained K-MeansCode1
Self-Supervised Classification NetworkCode1
Combining pretrained CNN feature extractors to enhance clustering of complex natural images—0
AC-VAE: Learning Semantic Representation with VAE for Adaptive Clustering—0
Improving Unsupervised Image Clustering With Robust LearningCode1
A Pseudo-labelling Auto-Encoder for unsupervised image classification—0
Unsupervised part representation by Flow Capsules—0
Unsupervised Image Classification Through Time-Multiplexed Photonic Multi-Layer Spiking Convolutional Neural Network—0
Improving Self-Organizing Maps with Unsupervised Feature ExtractionCode1
Self-Supervised Learning for Large-Scale Unsupervised Image ClusteringCode1
Unsupervised Feature Learning by Cross-Level Instance-Group DiscriminationCode1
Mitigating Embedding and Class Assignment Mismatch in Unsupervised Image ClassificationCode1
MIX'EM: Unsupervised Image Classification using a Mixture of Embeddings—0
Unsupervised Image Classification for Deep Representation LearningCode0
Deep Transformation-Invariant ClusteringCode1
SCAN: Learning to Classify Images without LabelsCode2
Self-supervised classification of dynamic obstacles using the temporal information provided by videos—0
Invariant Information Clustering for Unsupervised Image Classification and SegmentationCode1
Inferencing Based on Unsupervised Learning of Disentangled RepresentationsCode0
Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization—0
PixelGAN AutoencodersCode0
Learning Discrete Representations via Information Maximizing Self-Augmented TrainingCode0
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial NetsCode1
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial NetworksCode0
Unsupervised Deep Embedding for Clustering AnalysisCode1
Adversarial AutoencodersCode1
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