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
GUIDED MCMC FOR SPARSE BAYESIAN MODELS TO DETECT RARE EVENTS IN IMAGES SANS LABELED DATA—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
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