  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.
