Bayesian DivideMix++ for Enhanced Learning with Noisy Labels.

Leveraging inexpensive and human intervention-based annotating methodologies, such as crowdsourcing and web crawling, often leads to datasets with noisy labels. Noisy labels can have a detrimental impact on the performance and generalization of deep neural networks. Robust models that are able to handle and mitigate the effect of these noisy labels are thus essential.

Uncertainty-aware selecting for an ensemble of deep food recognition models.

Deep learning is a machine learning technique that has revolutionized the research community due to its impressive results on various real-life problems. Recently, ensembles of Convolutional Neural Networks (CNN) have proven to achieve high robustness and accuracy in numerous computer vision challenges. As expected, the more models we add to the ensemble, the better performance we can obtain, but, in contrast, more computer resources are needed.