SOTAVerified

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 26–45 of 45 papers

TitleStatusHype
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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