Although multi-label learning can deal with many problems with label ambiguity, it does not
fit some real applications well where the overall distribution of the importance of the labels …

Label correlation is helpful to alleviate the overwhelming output space of label distribution
learning (LDL). However, existing studies either only consider one of global and local label …

Label distribution covers a certain number of labels, representing the degree to which each
label describes the instance. When dealing with label ambiguity, label distribution could …

Recently, the deep learning technology has been adopted in the study of traditional village
landscape. More precisely, it's usually used to explore the representation of cultural heritage …

Label distribution learning (LDL) helps solve label ambiguity and has found wide
applications. However, it may suffer from the challenge of objective inconsistency when …

Label distribution learning (LDL) is a general learning framework, which assigns to an
instance a distribution over a set of labels rather than a single label or multiple labels …

Facial pose estimation refers to the task of predicting face orientation from a single RGB
image. It is an important research topic with a wide range of applications in computer vision …

Label enhancement (LE) is still a challenging task to mitigate the dilemma of the lack of label
distribution. Existing LE work typically focuses on primarily formulating a projection between …

Label distribution learning (LDL) is a newly arisen machine learning method that has been
increasingly studied in recent years. In theory, LDL can be seen as a generalization of multi …