A Simple Generative Network
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2021
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| _version_ | 1866909317949554688 |
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| author | Nissani, Daniel N. |
| author_facet | Nissani, Daniel N. |
| contents | Generative neural networks are able to mimic intricate probability distributions such as those of handwritten text, natural images, etc. Since their inception several models were proposed. The most successful of these were based on adversarial (GAN), auto-encoding (VAE) and maximum mean discrepancy (MMD) relatively complex architectures and schemes. Surprisingly, a very simple architecture (a single feed-forward neural network) in conjunction with an obvious optimization goal (Kullback_Leibler divergence) was apparently overlooked. This paper demonstrates that such a model (denoted SGN for its simplicity) is able to generate samples visually and quantitatively competitive as compared with the fore-mentioned state of the art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2106_09330 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | A Simple Generative Network Nissani, Daniel N. Machine Learning Neural and Evolutionary Computing Generative neural networks are able to mimic intricate probability distributions such as those of handwritten text, natural images, etc. Since their inception several models were proposed. The most successful of these were based on adversarial (GAN), auto-encoding (VAE) and maximum mean discrepancy (MMD) relatively complex architectures and schemes. Surprisingly, a very simple architecture (a single feed-forward neural network) in conjunction with an obvious optimization goal (Kullback_Leibler divergence) was apparently overlooked. This paper demonstrates that such a model (denoted SGN for its simplicity) is able to generate samples visually and quantitatively competitive as compared with the fore-mentioned state of the art methods. |
| title | A Simple Generative Network |
| topic | Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2106.09330 |