# Deep Learning for Multi-Label Learning: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2401.16549 · HTML: https://mlsanity.com/paper/2401.16549/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 58 of its 210 references that are in the map.
- First posted on arXiv 29 Jan 2024.

## Builds on (most cited first, 30 of 58)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Multi-Label Image Recognition with Graph Convolutional Networks](https://mlsanity.com/paper/1904.03582/) (2019)
- [CNN-RNN: A Unified Framework for Multi-label Image Classification](https://mlsanity.com/paper/1604.04573/) (2016)
- [Query2Label: A Simple Transformer Way to Multi-Label Classification](https://mlsanity.com/paper/2107.10834/) (2021)
- [General Multi-label Image Classification with Transformers](https://mlsanity.com/paper/2011.14027/) (2020)
- [Learning a Deep ConvNet for Multi-label Classification with Partial Labels](https://mlsanity.com/paper/1902.09720/) (2019)
- [Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition](https://mlsanity.com/paper/1908.07325/) (2019)
- [AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification](https://mlsanity.com/paper/1811.01727/) (2018)
- [The Emerging Trends of Multi-Label Learning](https://mlsanity.com/paper/2011.11197/) (2020)
- [Learning to Diagnose with LSTM Recurrent Neural Networks](https://mlsanity.com/paper/1511.03677/) (2015)
- [SGM: Sequence Generation Model for Multi-label Classification](https://mlsanity.com/paper/1806.04822/) (2018)
- [Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification](https://mlsanity.com/paper/1702.05891/) (2017)
- [ML-Decoder: Scalable and Versatile Classification Head](https://mlsanity.com/paper/2111.12933/) (2021)
- [Multi-Label Zero-Shot Learning with Structured Knowledge Graphs](https://mlsanity.com/paper/1711.06526/) (2017)
- [Multi-Label Classification with Label Graph Superimposing](https://mlsanity.com/paper/1911.09243/) (2019)
- [Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification](https://mlsanity.com/paper/2210.12843/) (2022)
- [CNN: Single-label to Multi-label](https://mlsanity.com/paper/1406.5726/) (2014)
- [Deep Convolutional Ranking for Multilabel Image Annotation](https://mlsanity.com/paper/1312.4894/) (2013)
- [Orderless Recurrent Models for Multi-label Classification](https://mlsanity.com/paper/1911.09996/) (2019)
- [Recurrent Attentional Reinforcement Learning for Multi-label Image Recognition](https://mlsanity.com/paper/1712.07465/) (2017)
- [Multi-Label Learning with Global and Local Label Correlation](https://mlsanity.com/paper/1704.01415/) (2017)
- [Improving Pairwise Ranking for Multi-label Image Classification](https://mlsanity.com/paper/1704.03135/) (2017)
- [Exploit Bounding Box Annotations for Multi-label Object Recognition](https://mlsanity.com/paper/1504.05843/) (2015)
- [Comprehensive Comparative Study of Multi-Label Classification Methods](https://mlsanity.com/paper/2102.07113/) (2021)
- [DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents](https://mlsanity.com/paper/2111.06685/) (2021)

## Built on by (most cited first, 19 of 19)

- [MAT-Agent: Adaptive Multi-Agent Training Optimization](https://mlsanity.com/paper/2510.17845/) (2025)
- [Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classification](https://mlsanity.com/paper/2411.17350/) (2024)
- [SATA-BENCH: Select All That Apply Benchmark for Multiple Choice Questions](https://mlsanity.com/paper/2506.00643/) (2025)
- [MILD: Multi-Intent Learning and Disambiguation for Proactive Failure Prediction in Intent-based Networking](https://mlsanity.com/paper/2602.14283/) (2026)
- [Large Language Models for Zero-Shot Multicultural Name Recognition](https://mlsanity.com/paper/2507.04149/) (2025)
- [Divide, Cache, Conquer: Dichotomic Prompting for Efficient Multi-Label LLM-Based Classification](https://mlsanity.com/paper/2511.03830/) (2025)
- [KVpop -- Key-Value Cache Compression with Predictive Online Pruning](https://mlsanity.com/paper/2607.05061/) (2026)
- [A Survey on Incomplete Multi-label Learning: Recent Advances and Future Trends](https://mlsanity.com/paper/2406.06119/) (2024)
- [Dual-Label Learning With Irregularly Present Labels](https://mlsanity.com/paper/2410.14380/) (2024)
- [Your Next State-of-the-Art Could Come from Another Domain: A Cross-Domain Analysis of Hierarchical Text Classification](https://mlsanity.com/paper/2412.12744/) (2024)
- [Identifying Obfuscated Code through Graph-Based Semantic Analysis of Binary Code](https://mlsanity.com/paper/2504.01481/) (2025)
- [Out-of-Distribution Detection via Channelwise Feature Aggregation in Neural Network-Based Receivers](https://mlsanity.com/paper/2505.15570/) (2025)
- [D2ACE: Multi-Label Batch Selection Guided by Dual Dynamics and Adaptive Correlation Enhancement](https://mlsanity.com/paper/2605.09400/) (2026)
- [Principled Algorithms for Optimizing Generalized Metrics in Multi-Label Learning](https://mlsanity.com/paper/2605.28767/) (2026)
- [Peak-Based Nuclide Identification in HPGe γ-Spectrometry with Machine Learning and SHAP](https://mlsanity.com/paper/2606.14874/) (2026)
- [Rethinking Multi-Label Image Classification With Deep Learning: Taxonomy, Challenge, and Outlook](https://mlsanity.com/paper/2607.00839/) (2026)
- [Untangling Co-Drift: Proactive Multi-Intent Failure Prediction and Root-Cause Disambiguation for Self-Driving Networks](https://mlsanity.com/paper/2607.25989/) (2026)
- [LightTeaNet: A Weakly Supervised Lightweight CNN for Multi-Label Tea Leaf Disease Detection and Localization](https://mlsanity.com/paper/2608.14178/) (2026)
- [Epistemic Learning from Imprecise Annotation](https://mlsanity.com/paper/2609.34285/) (2026)

## Abstract (arXiv)

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.