| Autor: |
Youngsung Kim, Yoonsuk Hyun, Jae-Joon Han, Eunho Yang, Sung Ju Hwang, Jinwoo Shin |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 11, Pp 146208-146222 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2023.3346430 |
| Popis: |
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 classification layer, we aim to encourage discriminative generalization. We introduce a self-supervised grouping method designed to unveil hierarchical structures in scenarios with unknown hierarchy information. To maximize distances between weight vectors on multiple hyperspheres, we propose a novel metric that combines discrete and continuous measures. This regularization encourages diverse orientations, consequently leading to improved generalization. Extensive evaluations on datasets, including CUB200-2011, Stanford-Cars, CIFAR-100, and TinyImageNet, consistently demonstrate enhancements in classification performance compared to baseline settings. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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