  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 address these challenges
more effectively in MLC. Notably, there is a burgeoning effort to harness the
robust learning capabilities of DL for improved modelling of label dependencies
and other challenges in MLC. However, it is noteworthy that comprehensive
studies specifically dedicated to DL for multi-label learning are limited.
Thus, this survey aims to thoroughly review recent progress in DL for
multi-label learning, along with a summary of open research problems in MLC.
The review consolidates existing research efforts in DL for MLC,including deep
neural networks, transformers, autoencoders, and convolutional and recurrent
architectures. Finally, the study presents a comparative analysis of the
existing methods to provide insightful observations and stimulate future
research directions in this domain.
