# Label distribution learning via label correlation grid

arXiv: https://arxiv.org/abs/2210.08184 · HTML: https://mlsanity.com/paper/2210.08184/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 15 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [Label Distribution Learning](https://mlsanity.com/paper/1408.6027/) (2014)
- [Towards Speaker Age Estimation with Label Distribution Learning](https://mlsanity.com/paper/2202.11424/) (2022)
- [Generalized Label Enhancement with Sample Correlations](https://mlsanity.com/paper/2004.03104/) (2020)
- [Label Distribution Learning via Implicit Distribution Representation](https://mlsanity.com/paper/2209.13824/) (2022)

## Built on by (most cited first, 1 of 1)

- [Latent label distribution grid representation for modeling uncertainty](https://mlsanity.com/paper/2505.21180/) (2025)

## Abstract (arXiv)

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 Label Correlation Grid (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.