Deep Learning for Multi-Label Learning: A Comprehensive Survey. (arXiv:2401.16549v1 [cs.LG])

Multi-label learning is a rapidly growing research area that aims to predict
multiple labels from a single input data point. In the era of big data, tasks
involving multi-label classification (MLC) or ranking present significant and
intricate challenges, capturing considerable attention in diverse domains.
Inherent difficulties in MLC include dealing with high-dimensional data,
addressing label correlations, and handling partial labels, for which
conventional methods prove ineffective. Recent years have witnessed a notable
increase in adopting deep learning (DL) techniques to …

arxiv attention big big data challenges classification cs.lg data deep learning diverse domains labels mlc multiple ranking research survey tasks