SEAL: Simultaneous Label Hierarchy Exploration And Learning
Abstract: Label hierarchy is an important source of external knowledge that can enhance classification performance. However, most existing methods rely on predefined label hierarchies that may not match the data distribution. To address this issue, we propose Simultaneous label hierarchy Exploration And Learning (SEAL), a new framework that explores the label hierarchy by augmenting the observed labels with latent labels that follow a prior hierarchical structure. Our approach uses a 1-Wasserstein metric over the tree metric space as an objective function, which enables us to simultaneously learn a data-driven label hierarchy and perform (semi-)supervised learning. We evaluate our method on several standard benchmarks and show that it achieves improved results in semi-supervised image classification scenarios.
Submission Length: Regular submission (no more than 12 pages of main content)
Previous TMLR Submission Url: https://openreview.net/forum?id=5h2bDS596L
Changes Since Last Submission: The previous submission doesn't follow TMLR's stylefile format (notably the font isn't the right one), we fix the font problem.
Assigned Action Editor: ~Yu_Yao3
Submission Number: 2408
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