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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2019
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/1909.04474 |
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| _version_ | 1866929485159333888 |
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| author | Wieluch, Sabine Schwenker, Friedhelm |
| author_facet | Wieluch, Sabine Schwenker, Friedhelm |
| contents | This paper demonstrates how Dropout can be used in Generative Adversarial Networks to generate multiple different outputs to one input. This method is thought as an alternative to latent space exploration, especially if constraints in the input should be preserved, like in A-to-B translation tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1909_04474 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Dropout Induced Noise for Co-Creative GAN Systems Wieluch, Sabine Schwenker, Friedhelm Machine Learning This paper demonstrates how Dropout can be used in Generative Adversarial Networks to generate multiple different outputs to one input. This method is thought as an alternative to latent space exploration, especially if constraints in the input should be preserved, like in A-to-B translation tasks. |
| title | Dropout Induced Noise for Co-Creative GAN Systems |
| topic | Machine Learning |
| url | https://arxiv.org/abs/1909.04474 |