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Found 3 papers, 1 papers with code
Uncertainty-guided Open-Set Source-Free Unsupervised Domain Adaptation with Target-private Class Segregation
We propose a novel approach for SF-OSDA that exploits the granularity of target-private categories by segregating their samples into multiple unknown classes.
Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation
We propose a novel approach for the SF-UDA setting based on a loss reweighting strategy that brings robustness against the noise that inevitably affects the pseudo-labels.
Single architecture and multiple task deep neural network for altered fingerprint analysis
Fingerprints are one of the most copious evidence in a crime scene and, for this reason, they are frequently used by law enforcement for identification of individuals.
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