  In contrast to multi-label learning, label distribution learning
characterizes the polysemy of examples by a label distribution to represent
richer semantics. In the learning process of label distribution, the training
data is collected mainly by manual annotation or label enhancement algorithms
to generate label distribution. Unfortunately, the complexity of the manual
annotation task or the inaccuracy of the label enhancement algorithm leads to
noise and uncertainty in the label distribution training set. To alleviate this
problem, we introduce the implicit distribution in the label distribution
learning framework to characterize the uncertainty of each label value.
Specifically, we use deep implicit representation learning to construct a label
distribution matrix with Gaussian prior constraints, where each row component
corresponds to the distribution estimate of each label value, and this row
component is constrained by a prior Gaussian distribution to moderate the noise
and uncertainty interference of the label distribution dataset. Finally, each
row component of the label distribution matrix is transformed into a standard
label distribution form by using the self-attention algorithm. In addition,
some approaches with regularization characteristics are conducted in the
training phase to improve the performance of the model.
