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