SUNLayer: Stable denoising with generative networks
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2018
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| _version_ | 1866917282996813824 |
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| author | Jin, Ruhui Mixon, Dustin G. Villar, Soledad |
| author_facet | Jin, Ruhui Mixon, Dustin G. Villar, Soledad |
| contents | Deep neural networks are often used to implement powerful generative models for real-world data. Notable applications include image denoising, as well as other classical inverse problems like compressed sensing and super-resolution. To provide a rigorous but simplified analysis of generative models, in this work, we introduce an elegant theoretical framework based on spherical harmonics, namely \textbf{SUNLayer}. Our theoretical framework identifies explicit conditions on activation functions that guarantee denoising under local optimization. Numerical experiments examine the theoretical properties on commonly used activation functions and demonstrate their stable denoising performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1803_09319 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | SUNLayer: Stable denoising with generative networks Jin, Ruhui Mixon, Dustin G. Villar, Soledad Machine Learning Deep neural networks are often used to implement powerful generative models for real-world data. Notable applications include image denoising, as well as other classical inverse problems like compressed sensing and super-resolution. To provide a rigorous but simplified analysis of generative models, in this work, we introduce an elegant theoretical framework based on spherical harmonics, namely \textbf{SUNLayer}. Our theoretical framework identifies explicit conditions on activation functions that guarantee denoising under local optimization. Numerical experiments examine the theoretical properties on commonly used activation functions and demonstrate their stable denoising performance. |
| title | SUNLayer: Stable denoising with generative networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/1803.09319 |