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 31–40 of 45 papers

TitleStatusHype
Unsupervised Image Classification Through Time-Multiplexed Photonic Multi-Layer Spiking Convolutional Neural Network—0
LatentGAN Autoencoder: Learning Disentangled Latent Distribution—0
Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization—0
MIX'EM: Unsupervised Image Classification using a Mixture of Embeddings—0
PixelGAN Autoencoders—0
Contrastive Knowledge Amalgamation for Unsupervised Image Classification—0
Revisiting the Transferability of Supervised Pretraining: an MLP Perspective—0
GUIDED MCMC FOR SPARSE BAYESIAN MODELS TO DETECT RARE EVENTS IN IMAGES SANS LABELED DATA—0
The VampPrior Mixture ModelCode0
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial NetworksCode0
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