Reducing Deep Network Complexity via Sparse Hierarchical Fourier Interaction Networks
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2017
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916694878846976 |
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| author | Kiruluta, Andrew Williams, Samantha |
| author_facet | Kiruluta, Andrew Williams, Samantha |
| contents | This paper presents a Sparse Hierarchical Fourier Interaction Networks, an architectural building block that unifies three complementary principles of frequency domain modeling: A hierarchical patch wise Fourier transform that affords simultaneous access to local detail and global context; A learnable, differentiable top K masking mechanism which retains only the most informative spectral coefficients, thereby exploiting the natural compressibility of visual and linguistic signals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1801_01451 |
| institution | arXiv |
| publishDate | 2017 |
| record_format | arxiv |
| spellingShingle | Reducing Deep Network Complexity via Sparse Hierarchical Fourier Interaction Networks Kiruluta, Andrew Williams, Samantha Computer Vision and Pattern Recognition Machine Learning This paper presents a Sparse Hierarchical Fourier Interaction Networks, an architectural building block that unifies three complementary principles of frequency domain modeling: A hierarchical patch wise Fourier transform that affords simultaneous access to local detail and global context; A learnable, differentiable top K masking mechanism which retains only the most informative spectral coefficients, thereby exploiting the natural compressibility of visual and linguistic signals. |
| title | Reducing Deep Network Complexity via Sparse Hierarchical Fourier Interaction Networks |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/1801.01451 |