Abstract
Partial label learning (PLL) is a specific weakly supervised learning problem, where each training example is associated with a set of candidate labels while only one of them is the ground truth. Recently, a disambiguation-free partial label learning method based on error-correcting output codes has been proposed, and achieves outstanding performance among existing partial label learning methods. Despite its good performance, it cannot deal with high ambiguity scenario and large candidate label size. To tackle this issue, we propose a new partial label learning method called PL-GECOC that gradually induces error-correction output codes during iterative model training. Experiments show that PL-GECOC outperforms most of the existing methods, especially in high ambiguity and large candidate label size scenarios.
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Acknowledgements
The authors wish to thank the anonymous reviewers for their helpful comments and suggestions. This work was supported by the National Science Foundation of China (62176055, 62225602).
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Shi, YX., Wang, DB., Zhang, ML. (2023). Partial Label Learning with Gradually Induced Error-Correction Output Codes. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13623. Springer, Cham. https://doi.org/10.1007/978-3-031-30105-6_17
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