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.

翻译：与多标签学习不同,标签分配学习通过标签分布来代表更富的语义。在标签分布的学习过程中,培训数据主要通过人工注解或标签增强算法收集,以产生标签分布。不幸的是,手册注解任务的复杂性或标签增强算法的不准确性导致标签分发培训的噪音和不确定性。为了缓解这一问题,我们在标签分布学习框架中引入了隐含的分布,以说明每个标签值的不确定性。具体地说,我们利用深度隐含的表达学习来构建标签分配矩阵,使用高山先前的限制,其中每个行的成分对应每个标签值的分布估计值,而这一行组件受先前高山分布的制约,以缓和标签分发数据集的噪音和不确定性干扰。最后,标签分配矩阵的每行组成部分都通过自省算法转换成标准标签分配格式。此外,在培训阶段还采用了一些具有正规化特点的方法,以改进模型的性能。