Exploiting Related and Unrelated Tasks for Hierarchical Metric Learning and Image Classification
In multi-task learning, multiple interrelated tasks are jointly learned to achieve better performance. In many cases, if we can identify which tasks are related, we can also clearly identify which tasks are unrelated. In the past, most researchers emphasized exploiting correlations among interrelate...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - (2019) vom: 05. Sept. |
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| Weitere Verfasser: | , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2019
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article |
| Zusammenfassung: | In multi-task learning, multiple interrelated tasks are jointly learned to achieve better performance. In many cases, if we can identify which tasks are related, we can also clearly identify which tasks are unrelated. In the past, most researchers emphasized exploiting correlations among interrelated tasks while completely ignoring the unrelated tasks that may provide valuable prior knowledge for multi-task learning. In this paper, a new approach is developed to hierarchically learn a tree of multi-task metrics by leveraging prior knowledge about both the related tasks and unrelated tasks. First, a visual tree is constructed to hierarchically organize large numbers of image categories in a coarse-to-fine fashion. Over the visual tree, a multi-task metric classifier is learned for each node by exploiting both the related and unrelated tasks, where the learning tasks for training the classifiers for the sibling child nodes under the same parent node are treated as the interrelated tasks, and the others are treated as the unrelated tasks. In addition, the node-specific metric for the parent node is propagated to its sibling child nodes to control inter-level error propagation. Our experimental results demonstrate that our hierarchical metric learning algorithm achieves better results than other state-of-the-art algorithms |
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| Beschreibung: | Date Revised 27.02.2024 published: Print-Electronic Citation Status Publisher |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2019.2938321 |