Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework

• URL: http://arxiv.org/abs/2204.13207v1
• Date: Wed, 27 Apr 2022 21:41:44 GMT
• Title: Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework
• Authors: Shu Zhang and Ran Xu and Caiming Xiong and Chetan Ramaiah
• Abstract summary: 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.
• Score: 75.79736930414715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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