Unlocking the Power of Open Set: A New Perspective for Open-Set Noisy Label Learning
DOI:
https://doi.org/10.1609/aaai.v38i14.29469Keywords:
ML: Classification and Regression, ML: Deep Learning Algorithms, ML: Other Foundations of Machine Learning, ML: Representation Learning, ML: Semi-Supervised Learning, ML: Unsupervised & Self-Supervised LearningAbstract
Learning from noisy data has attracted much attention, where most methods focus on closed-set label noise. However, a more common scenario in the real world is the presence of both open-set and closed-set noise. Existing methods typically identify and handle these two types of label noise separately by designing a specific strategy for each type. However, in many real-world scenarios, it would be challenging to identify open-set examples, especially when the dataset has been severely corrupted. Unlike the previous works, we explore how models behave when faced with open-set examples, and find that a part of open-set examples gradually get integrated into certain known classes, which is beneficial for the separation among known classes. Motivated by the phenomenon, we propose a novel two-step contrastive learning method CECL (Class Expansion Contrastive Learning) which aims to deal with both types of label noise by exploiting the useful information of open-set examples. Specifically, we incorporate some open-set examples into closed-set classes to enhance performance while treating others as delimiters to improve representative ability. Extensive experiments on synthetic and real-world datasets with diverse label noise demonstrate the effectiveness of CECL.Downloads
Published
2024-03-24
How to Cite
Wan, W., Wang, X., Xie, M.-K., Li, S.-Y., Huang, S.-J., & Chen, S. (2024). Unlocking the Power of Open Set: A New Perspective for Open-Set Noisy Label Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 38(14), 15438-15446. https://doi.org/10.1609/aaai.v38i14.29469
Issue
Section
AAAI Technical Track on Machine Learning V