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 11–20 of 45 papers

TitleStatusHype
Minimalistic Unsupervised Learning with the Sparse Manifold Transform—0
Capsule Network based Contrastive Learning of Unsupervised Visual RepresentationsCode1
Loss Function Entropy Regularization for Diverse Decision Boundaries—0
LatentGAN Autoencoder: Learning Disentangled Latent Distribution—0
DeepDPM: Deep Clustering With an Unknown Number of ClustersCode2
Revisiting the Transferability of Supervised Pretraining: an MLP Perspective—0
iBOT: Image BERT Pre-Training with Online TokenizerCode1
Self-Supervised Learning by Estimating Twin Class DistributionsCode1
GUIDED MCMC FOR SPARSE BAYESIAN MODELS TO DETECT RARE EVENTS IN IMAGES SANS LABELED DATA—0
Unsupervised Visual Representation Learning by Online Constrained K-MeansCode1
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