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Label distribution learning via label correlation grid

2022-10-15 03:58:15
Qimeng Guo, Zhuoran Zheng, Xiuyi Jia, Liancheng Xu

Abstract

Label distribution learning can characterize the polysemy of an instance through label distributions. However, some noise and uncertainty may be introduced into the label space when processing label distribution data due to artificial or environmental factors. To alleviate this problem, we propose a \textbf{L}abel \textbf{C}orrelation \textbf{G}rid (LCG) to model the uncertainty of label relationships. Specifically, we compute a covariance matrix for the label space in the training set to represent the relationships between labels, then model the information distribution (Gaussian distribution function) for each element in the covariance matrix to obtain an LCG. Finally, our network learns the LCG to accurately estimate the label distribution for each instance. In addition, we propose a label distribution projection algorithm as a regularization term in the model training process. Extensive experiments verify the effectiveness of our method on several real benchmarks.

Abstract (translated)

URL

https://arxiv.org/abs/2210.08184

PDF

https://arxiv.org/pdf/2210.08184.pdf


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