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 21–30 of 45 papers

TitleStatusHype
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
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