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
Adversarial AutoencodersCode1
IPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised Feature RepresentationCode0
The VampPrior Mixture ModelCode0
Contrastive Knowledge Amalgamation for Unsupervised Image Classification—0
ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision—0
MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillationCode0
Minimalistic Unsupervised Learning with the Sparse Manifold Transform—0
Loss Function Entropy Regularization for Diverse Decision Boundaries—0
LatentGAN Autoencoder: Learning Disentangled Latent Distribution—0
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