Divide and Conquer: Learning Label Distribution with Subtasks

Haitao Wu, Weiwei Li, Xiuyi Jia
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:67408-67426, 2025.

Abstract

Label distribution learning (LDL) is a novel learning paradigm that emulates label polysemy by assigning label distributions over the label space. However, recent LDL work seems to exhibit a notable contradiction: 1) existing LDL methods employ auxiliary tasks to enhance performance, which narrows their focus to specific applications, thereby lacking generalizability; 2) conversely, LDL methods without auxiliary tasks rely on losses tailored solely to the primary task, lacking beneficial data to guide the learning process. In this paper, we propose S-LDL, a novel and minimalist solution that generates subtask label distributions, i.e., a form of extra supervised information, to reconcile the above contradiction. S-LDL encompasses two key aspects: 1) an algorithm capable of generating subtasks without any prior/expert knowledge; and 2) a plug-andplay framework seamlessly compatible with existing LDL methods, and even adaptable to derivative tasks of LDL. Our analysis and experiments demonstrate that S-LDL is effective and efficient. To the best of our knowledge, this paper represents the first endeavor to address LDL via subtasks.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-wu25p, title = {Divide and Conquer: Learning Label Distribution with Subtasks}, author = {Wu, Haitao and Li, Weiwei and Jia, Xiuyi}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {67408--67426}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/wu25p/wu25p.pdf}, url = {https://proceedings.mlr.press/v267/wu25p.html}, abstract = {Label distribution learning (LDL) is a novel learning paradigm that emulates label polysemy by assigning label distributions over the label space. However, recent LDL work seems to exhibit a notable contradiction: 1) existing LDL methods employ auxiliary tasks to enhance performance, which narrows their focus to specific applications, thereby lacking generalizability; 2) conversely, LDL methods without auxiliary tasks rely on losses tailored solely to the primary task, lacking beneficial data to guide the learning process. In this paper, we propose S-LDL, a novel and minimalist solution that generates subtask label distributions, i.e., a form of extra supervised information, to reconcile the above contradiction. S-LDL encompasses two key aspects: 1) an algorithm capable of generating subtasks without any prior/expert knowledge; and 2) a plug-andplay framework seamlessly compatible with existing LDL methods, and even adaptable to derivative tasks of LDL. Our analysis and experiments demonstrate that S-LDL is effective and efficient. To the best of our knowledge, this paper represents the first endeavor to address LDL via subtasks.} }
Endnote
%0 Conference Paper %T Divide and Conquer: Learning Label Distribution with Subtasks %A Haitao Wu %A Weiwei Li %A Xiuyi Jia %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-wu25p %I PMLR %P 67408--67426 %U https://proceedings.mlr.press/v267/wu25p.html %V 267 %X Label distribution learning (LDL) is a novel learning paradigm that emulates label polysemy by assigning label distributions over the label space. However, recent LDL work seems to exhibit a notable contradiction: 1) existing LDL methods employ auxiliary tasks to enhance performance, which narrows their focus to specific applications, thereby lacking generalizability; 2) conversely, LDL methods without auxiliary tasks rely on losses tailored solely to the primary task, lacking beneficial data to guide the learning process. In this paper, we propose S-LDL, a novel and minimalist solution that generates subtask label distributions, i.e., a form of extra supervised information, to reconcile the above contradiction. S-LDL encompasses two key aspects: 1) an algorithm capable of generating subtasks without any prior/expert knowledge; and 2) a plug-andplay framework seamlessly compatible with existing LDL methods, and even adaptable to derivative tasks of LDL. Our analysis and experiments demonstrate that S-LDL is effective and efficient. To the best of our knowledge, this paper represents the first endeavor to address LDL via subtasks.
APA
Wu, H., Li, W. & Jia, X.. (2025). Divide and Conquer: Learning Label Distribution with Subtasks. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:67408-67426 Available from https://proceedings.mlr.press/v267/wu25p.html.

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