Label distribution learning (LDL) is a new machine learning paradigm for solving label ambiguity. Since it is difficult to directly obtain label distributions, many studies are focusing on how to recover label distributions from logical labels, dubbed label enhancement (LE). Existing LE methods estimate label distributions by simply building a mapping relationship between features and label distributions under the supervision of logical labels. They typically overlook the fact that both features and logical labels are descriptions of the instance from different views. Therefore, we propose a novel method called Contrastive Label Enhancement (ConLE) which integrates features and logical labels into the unified projection space to generate high-level features by contrastive learning strategy. In this approach, features and logical labels belonging to the same sample are pulled closer, while those of different samples are projected farther away from each other in the projection space. Subsequently, we leverage the obtained high-level features to gain label distributions through a welldesigned training strategy that considers the consistency of label attributes. Extensive experiments on LDL benchmark datasets demonstrate the effectiveness and superiority of our method.

翻译：标签分布学习是一种解决标签模糊性的新型机器学习范式。由于难以直接获取标签分布，众多研究聚焦于如何从逻辑标签中恢复标签分布，这被称为标签增强。现有标签增强方法通常在逻辑标签的监督下，通过简单构建特征与标签分布之间的映射关系来估计标签分布，却往往忽略了特征与逻辑标签本质上是从不同视角对样本的描述这一事实。为此，我们提出一种名为对比标签增强的创新方法，该方法通过对比学习策略将特征与逻辑标签整合到统一投影空间中生成高层特征。在该投影空间中，相同样本的特征与逻辑标签被拉近，而不同样本的特征与逻辑标签则被推远。随后，我们利用所获得的高层特征，通过考虑标签属性一致性的精心设计训练策略来获取标签分布。在标签分布学习基准数据集上的大量实验表明，该方法具有显著的有效性与优越性。