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Found 2 papers, 2 papers with code
Representation Learning via Consistent Assignment of Views over Random Partitions
We extensively ablate our method and demonstrate that our proposed random partition pretext task improves the quality of the learned representations by devising multiple random classification tasks.
Representation Learning via Consistent Assignment of Views to Clusters
We introduce Consistent Assignment for Representation Learning (CARL), an unsupervised learning method to learn visual representations by combining ideas from self-supervised contrastive learning and deep clustering.
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