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A Novel Orthogonality Loss for Deep Hierarchical Multi-Task Learning
2020
IEEE Access
In this paper, a novel loss function is proposed to measure the correlation among different learning tasks and select useful feature components for each classification task. Firstly, the knowledge map we proposed is used for organizing the affiliation relationship between objects in natural world. Secondly, a novel loss function-orthogonality loss is proposed to make the deep features more discriminative by removing useless feature components. Furthermore, in order to prevent the extracted
doi:10.1109/access.2020.2985991
fatcat:y5bvda5v2vcirnv4wbx5ztifum