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
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 Autoencoders—0
Learning Discrete Representations via Information Maximizing Self-Augmented TrainingCode0
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial NetworksCode0
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