Partial label learning (PLL) is an emerging framework in weakly supervised machine
learning with broad application prospects. It handles the case in which each training …

Classical machine learning implicitly assumes that labels of the training data are sampled
from a clean distribution, which can be too restrictive for real-world scenarios. However …

Partial-label learning (PLL) is a multi-class classification problem, where each training
example is associated with a set of candidate labels. Even though many practical PLL …

Partial-label learning (PLL) is a typical weakly supervised learning problem, where each
training instance is equipped with a set of candidate labels among which only one is the true …

Partial label learning (PLL) is a typical weakly supervised learning problem, where each
training example is associated with a set of candidate labels among which only one is true …

Overparameterized deep networks have the capacity to memorize training data with
zero\emph {training error}. Even after memorization, the\emph {training loss} continues to …

As an important branch of weakly supervised learning, partial label learning deals with data
where each instance is assigned with a set of candidate labels, whereas only one of them is …

In unsupervised domain adaptation (UDA), a classifier for the target domain is trained with
massive true-label data from the source domain and unlabeled data from the target domain …

A complementary label (CL) simply indicates an incorrect class of an example, but learning
with CLs results in multi-class classifiers that can predict the correct class. Unfortunately, the …

Given data with noisy labels, over-parameterized deep networks can gradually memorize
the data, and fit everything in the end. Although equipped with corrections for noisy labels …