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.

该论文提出了一种新的机器学习范例——标签分布学习（LDL），并设计了一个名为Contrastive Label Enhancement（ConLE）的方法，该方法利用对比学习策略将特征和逻辑标签融合到统一的投影空间中，生成高层次特征，然后通过一种有效的训练策略获得标签分布，该方法在LDL基准数据集上进行了广泛实验，表明其在效果和优越性方面具有很大的潜力。

对比度标签增强