Summary of Deep Learning For Multi-label Learning: a Comprehensive Survey, by Adane Nega Tarekegn et al.
Deep Learning for Multi-Label Learning: A Comprehensive Survey
by Adane Nega Tarekegn, Mohib Ullah, Faouzi Alaya Cheikh
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Multi-label learning has emerged as a crucial area of research in big data era, tackling complex challenges such as high-dimensional data, label correlations, and partial labels. Conventional methods struggle with these difficulties, whereas deep learning (DL) techniques show promise in addressing them more effectively. Recent progress includes harnessing DL for modelling label dependencies, but comprehensive studies on DL for multi-label learning are limited. This survey aims to review recent advancements in DL for multi-label learning, summarizing open research problems and consolidating existing efforts including deep neural networks, transformers, autoencoders, and convolutional and recurrent architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers explore the use of deep learning (DL) for multi-label learning. Multi-label learning is a big challenge in today’s data-driven world. It involves predicting multiple labels from one piece of data. Traditional methods don’t work well because they can’t handle high-dimensional data or label correlations. DL seems to be a good solution, but there isn’t much research on it yet. This paper reviews the recent progress and finds out what works and what doesn’t. |
Keywords
* Artificial intelligence * Deep learning
