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Improving learning with noisy labels in two possibile scenarios.

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Learning from data with noisy labels is a challenging problem that arises in various practical applications. Noisy data can indeed arise in various real-world problems, such as medical diagnosis, autonomous driving, fraud detection, and natural language processing. Its presence can significantly impact the accuracy and reliability of machine learning models. In this talk, we will introduce two different frameworks for improving learning with noisy labels in two possible scenarios. In the first scenario, we assume access to data labeled by multiple annotators. In the second scenario, only one label is given for each sample. For the first case, we will leverage inter-rater agreement to effectively mitigate the issue of noisy labels. In the second scenario, our framework proposes a novel approach that combines the use of class centroids and an outlier discounting strategy.

This talk is part of the Artificial Intelligence Research Group Talks (Computer Laboratory) series.

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