Deep Self-Supervised Diversity Promoting Learning on Hierarchical Hyperspheres for Regularization
In this paper, we propose a novel approach to enhance the generalization performance of deep neural networks. Our method employs a hierarchical hypersphere-based constraint that organizes weight vectors hierarchically based on observed data. By diversifying the parameter space of hyperplanes in the...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10373009/ |