Multi-Label Learning

81 papers with code • 1 benchmarks • 7 datasets

Multi-label learning (MLL) is a generalization of the binary and multi-category classification problems and deals with tagging a data instance with several possible class labels simultaneously [1]. Each of the assigned labels conveys a specific semantic relationship with the multi-label data instance [2, 3]. Multi-label learning has continued to receive a lot of research interest due to its practical application in many real-world problems such as recommender systems [4], image annotation [5], and text classification [6].

References:

  1. Kumar, S., Rastogi, R., Low rank label subspace transformation for multi-label learning with missing labels. Information Sciences 596, 53–72 (2022)

  2. Zhang M-L, Zhou Z-H (2013) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837

  3. Gibaja E, Ventura S (2015) A tutorial on multilabel learning. ACM Comput Surveys (CSUR) 47(3):1–38

  4. Bogaert M, Lootens J, Van den Poel D, Ballings M (2019) Evaluating multi-label classifiers and recommender systems in the financial service sector. Eur J Oper Res 279(2):620– 634

  5. Jing L, Shen C, Yang L, Yu J, Ng MK (2017) Multi-label classification by semi-supervised singular value decomposition. IEEE Trans Image Process 26(10):4612–4625

  6. Chen Z, Ren J (2021) Multi-label text classification with latent word-wise label information. Appl Intell 51(2):966–979

ProPML: Probability Partial Multi-label Learning

gmum/propml 12 Mar 2024

Partial Multi-label Learning (PML) is a type of weakly supervised learning where each training instance corresponds to a set of candidate labels, among which only some are true.

0
12 Mar 2024

MIML library: a Modular and Flexible Library for Multi-instance Multi-label Learning

kdis-lab/miml 12 Feb 2024

MIML library is a Java software tool to develop, test, and compare classification algorithms for multi-instance multi-label (MIML) learning.

9
12 Feb 2024

Vision-Language Pseudo-Labels for Single-Positive Multi-Label Learning

mvrl/vlpl 24 Oct 2023

In general multi-label learning, a model learns to predict multiple labels or categories for a single input image.

6
24 Oct 2023

Neural Collapse in Multi-label Learning with Pick-all-label Loss

heimine/nc_mlab 24 Oct 2023

We study deep neural networks for the multi-label classification (MLab) task through the lens of neural collapse (NC).

1
24 Oct 2023

Multi-Label Feature Selection Using Adaptive and Transformed Relevance

sadegh28/atr 26 Sep 2023

Multi-label learning has emerged as a crucial paradigm in data analysis, addressing scenarios where instances are associated with multiple class labels simultaneously.

2
26 Sep 2023

Multi-Label Noise Transition Matrix Estimation with Label Correlations: Theory and Algorithm

tmllab/Multi-Label-T 22 Sep 2023

However, estimating multi-label noise transition matrices remains a challenging task, as most existing estimators in noisy multi-class learning rely on anchor points and accurate fitting of noisy class posteriors, which is hard to satisfy in noisy multi-label learning.

11
22 Sep 2023

Multi-Label Knowledge Distillation

penghui-yang/l2d ICCV 2023

Existing knowledge distillation methods typically work by imparting the knowledge of output logits or intermediate feature maps from the teacher network to the student network, which is very successful in multi-class single-label learning.

19
12 Aug 2023

When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-$k$ Multi-Label Learning

yuchen-sunflower/tkmia 27 Jul 2023

However, existing adversarial attacks toward multi-label learning only pursue the traditional visual imperceptibility but ignore the new perceptible problem coming from measures such as Precision@$k$ and mAP@$k$.

1
27 Jul 2023

Semantic-Aware Dual Contrastive Learning for Multi-label Image Classification

yu-gi-oh-leilei/sadcl 19 Jul 2023

Specifically, we leverage semantic-aware representation learning to extract category-related local discriminative features and construct category prototypes.

15
19 Jul 2023

Minimal Learning Machine for Multi-Label Learning

jookriha/ml-mlm 9 May 2023

Distance-based supervised method, the minimal learning machine, constructs a predictive model from data by learning a mapping between input and output distance matrices.

2
09 May 2023