论文标题
使用所有标签：分层多标签对比学习框架
Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework
论文作者
论文摘要
当前的对比学习框架着眼于利用单个监督信号学习表示表示，这限制了看不见的数据和下游任务的功效。在本文中，我们提出了一个分层多标签表示学习框架，该框架可以利用所有可用的标签并保留类之间的层次关系。我们介绍了保留新的层次结构损失，该损失共同对对比损失施加层次惩罚，并执行层次结构约束。损耗函数是数据驱动的，并自动适应了任意的多标签结构。几个数据集的实验表明，我们提供关系的嵌入在各种任务上都表现良好，并且优于基线监督和自我监督的方法。代码可在https://github.com/salesforce/hierarchicalcontrastivelearning上找到。
Current contrastive learning frameworks focus on leveraging a single supervisory signal to learn representations, which limits the efficacy on unseen data and downstream tasks. In this paper, we present a hierarchical multi-label representation learning framework that can leverage all available labels and preserve the hierarchical relationship between classes. We introduce novel hierarchy preserving losses, which jointly apply a hierarchical penalty to the contrastive loss, and enforce the hierarchy constraint. The loss function is data driven and automatically adapts to arbitrary multi-label structures. Experiments on several datasets show that our relationship-preserving embedding performs well on a variety of tasks and outperform the baseline supervised and self-supervised approaches. Code is available at https://github.com/salesforce/hierarchicalContrastiveLearning.
