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Found 2 papers, 1 papers with code
PUMP: Pyramidal and Uniqueness Matching Priors for Unsupervised Learning of Local Descriptors
In this paper, we propose to explicitly integrate two matching priors in a single loss in order to learn local descriptors without supervision.
Learning From Long-Tailed Data With Noisy Labels
In addition, the resulting learned representations are also remarkably robust to label noise, when fine-tuned with an imbalance- and noise-resistant loss function.
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