Improving Model Robustness With Frequency Component Modification and Mixing
Deep neural networks are sensitive to distribution shifts, such as common corruption and adversarial examples, which occur across various frequency spectra. Numerous studies have been conducted to improve model robustness in the frequency domain. However, research that simultaneously addresses safet...
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| Principais autores: | , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
IEEE
2024-01-01
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| coleção: | IEEE Access |
| Assuntos: | |
| Acesso em linha: | https://ieeexplore.ieee.org/document/10776988/ |
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