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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
Mitigating Embedding and Class Assignment Mismatch in Unsupervised Image ClassificationCode1
MIX'EM: Unsupervised Image Classification using a Mixture of Embeddings—0
Unsupervised Image Classification for Deep Representation LearningCode0
Deep Transformation-Invariant ClusteringCode1
SCAN: Learning to Classify Images without LabelsCode2
Self-supervised classification of dynamic obstacles using the temporal information provided by videos—0
Invariant Information Clustering for Unsupervised Image Classification and SegmentationCode1
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 AutoencodersCode0
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