论文标题
基于等级的课程损失
Hierarchical Class-Based Curriculum Loss
论文作者
论文摘要
机器学习中的分类算法通常假设平坦的标签空间。但是，大多数现实世界数据之间都有依赖性标签，通常可以使用层次结构来捕获。利用这种关系可以帮助开发能够满足依赖关系并提高模型准确性和解释性的模型。此外，由于层次结构中的不同级别对应于不同的粒度，因此对每个标签的惩罚都可能不利于模型学习。在本文中，我们提出了一个损失函数，分层课程损失，具有两种特性：（i）满足标签空间中存在的层次结构约束，（ii）基于标签的层次结构中的标签提供了不均匀的权重，通过层次结构中的水平，通过训练范式隐含地学到了。从理论上讲，与满足分层约束的任何其他损失相比，提出的损失函数是0-1损失的更严格的结合。我们在现实世界图像数据集上测试损失功能，并表明它显着优于多个基线。
Classification algorithms in machine learning often assume a flat label space. However, most real world data have dependencies between the labels, which can often be captured by using a hierarchy. Utilizing this relation can help develop a model capable of satisfying the dependencies and improving model accuracy and interpretability. Further, as different levels in the hierarchy correspond to different granularities, penalizing each label equally can be detrimental to model learning. In this paper, we propose a loss function, hierarchical curriculum loss, with two properties: (i) satisfy hierarchical constraints present in the label space, and (ii) provide non-uniform weights to labels based on their levels in the hierarchy, learned implicitly by the training paradigm. We theoretically show that the proposed loss function is a tighter bound of 0-1 loss compared to any other loss satisfying the hierarchical constraints. We test our loss function on real world image data sets, and show that it significantly substantially outperforms multiple baselines.
