提出背景浓度概念，让标签分布预测更完整准确。
Concentration Distribution Learning from Label Distributions
- 引入背景浓度作为标签的绝对强度项，弥补标签分布缺失信息。
- 通过概率与神经网络模型从现有数据中学习标签分布与背景浓度。
- 在多个数据集上优于当前最优方法，适合需要精确描述度的任务。
标签分布学习（LDL）是一种有效预测样本标签描述程度的方法。然而，标签分布无法完整表征实例，因其忽略了每个标签的绝对强度。具体而言，无法获得未出现在标签空间中的隐藏标签的总描述度，导致信息丢失和实例混淆。为解决此问题，本文提出新概念‘背景浓度’，作为标签分布的绝对描述度项，并将其引入LDL流程，形成改进的浓度分布学习范式。此外，我们设计了一种基于概率方法与神经网络的新模型，可从现有LDL数据集中学习标签分布与背景浓度。大量实验表明，该方法能有效从标签分布中提取背景浓度，并在预测精度上优于当前最先进的LDL方法。代码已公开于 https://github.com/seutjw/CDL-LD。
原文摘要 · Abstract (English)
Label distribution learning (LDL) is an effective method to predict the relative label description degree (a.k.a. label distribution) of a sample. However, the label distribution is not a complete representation of an instance because it overlooks the absolute intensity of each label. Specifically, it's impossible to obtain the total description degree of hidden labels that not in the label space, which leads to the loss of information and confusion in instances. To solve the above problem, we come up with a new concept named background concentration to serve as the absolute description degree term of the label distribution and introduce it into the LDL process, forming the improved paradigm of concentration distribution learning. Moreover, we propose a novel model by probabilistic methods and neural networks to learn label distributions and background concentrations from existing LDL datasets. Extensive experiments prove that the proposed approach is able to extract background concentrations from label distributions while producing more accurate prediction results than the state-of-the-art LDL methods. The code is available in https://github.com/seutjw/CDL-LD.
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