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Found 2 papers, 1 papers with code
Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings
Unsupervised learning allows us to leverage unlabelled data, which has become abundantly available, and to create embeddings that are usable on a variety of downstream tasks.
A Framework using Contrastive Learning for Classification with Noisy Labels
We propose a framework using contrastive learning as a pre-training task to perform image classification in the presence of noisy labels.
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