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Found 2 papers, 1 papers with code
Multi-class Classification without Multi-class Labels
This work presents a new strategy for multi-class classification that requires no class-specific labels, but instead leverages pairwise similarity between examples, which is a weaker form of annotation.
A probabilistic constrained clustering for transfer learning and image category discovery
The proposed objective directly minimizes the negative log-likelihood of cluster assignment with respect to the pairwise constraints, has no hyper-parameters, and demonstrates improved scalability and performance on both supervised learning and unsupervised transfer learning.
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