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 41–45 of 45 papers

TitleStatusHype
Loss Function Entropy Regularization for Diverse Decision Boundaries—0
Minimalistic Unsupervised Learning with the Sparse Manifold Transform—0
MIX'EM: Unsupervised Image Classification using a Mixture of Embeddings—0
PixelGAN Autoencoders—0
Contrastive Knowledge Amalgamation for Unsupervised Image Classification—0
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