… we discuss four key differences between confident learning and INCV. First, INCV errors are
found using an iterative version of the Cconfusion confident learning baseline: any example …

… Creatively, We introduce confident learning (CL) method to identify the corrupted labels
and endow CNN an anti-interference ability to the noises. Specifically, the CL technique is …

… -assisted confident learning framework to robustly exploit the noisy labeled data for the
challenging hepatic vessel segmentation task. Specifically, with the adapted confident learning …

… section, a discussion about what confident learning is which will mainly consist in definitions…
model theory which underpins the problem with “confident learning”, and finally, a section on …

… In this work, we propose an EfficientCLIP method via Ensemble Confident Learning to obtain
a less noisy data subset. Extra rich non-paired single-modal text data is used for boosting …

… To address this issue, we propose a Mean-Teacher-assisted Confident Learning (MTCL)
framework constructed by a teacher-student architecture and a label self-denoising process to …

… This paper demonstrates that bias can be eliminated by implementing confident learning
and filtering the fairest instances, even with access to biased labels. However, challenges arise …

… To this end, we introduce confident learning whereby a machine (like humans) must learn
… We achieve this by developing a principled theory and framework for confident learning with …

We study on-line learning in the linear regression framework. Most of the performance bounds
for on-line algorithms in this framework assume a constant learning rate. To achieve these …

… Inspired by confident learning, we rank the pseudo-labels of … framework in combination with
confident learning, which can … target instances by using confident learning so that the target …