<p>Although the main stream research is on Generative Adversarial Networks(GANs) using Multi Layer Percepteons (MLPs). The original paper titled <a href="https://arxiv.org/pdf/1406.2661.pdf" rel="nofollow noreferrer">Generative Adversarial Nets</a> clealry says, in abstract, that GAN is possible with out MLP also</p>
<blockquote>
<p>In the <strong>space of arbitrary functions</strong> <span class="math-container">$G$</span> and <span class="math-container">$D$</span>, a unique solution
exists, with <span class="math-container">$G$</span> recovering the training data distribution and <span class="math-container">$D$</span>
equal to <span class="math-container">$\dfrac{1}{2}$</span> everywhere. In the case where G and D are defined
by multilayer perceptrons, the entire system can be trained with backpropagation.</p>
</blockquote>
<p>Are there any research papers that uses models other than MLPs and are comparatively successful?</p>

CC BY-SA 4.0
2021-08-01T21:24:35.170Z
[FCC-GAN: A Fully Connected and Convolutional Net
Architecture for GANs](https://arxiv.org/pdf/1905.02417.pdf)
Yahya
CC BY-SA 4.0
2021-08-01T21:32:38.663Z
@Yahya It is also using MLP right?
hanugm
CC BY-SA 4.0
2021-08-01T21:34:24.570Z
I mix of two, but mainly a Convolution NN, look page 4, Table 1, the right column, for the architecture.
Yahya
CC BY-SA 4.0
2021-08-01T21:43:54.147Z
@Yahya Oh! In the question, i used MLP as a representative term for all forms of neural networks.
hanugm
CC BY-SA 4.0
2021-08-01T21:45:11.670Z
So Do you mean a non-ANN based model, such as a SVM or a simple Linear Model?
Yahya
CC BY-SA 4.0
2021-08-01T21:47:04.040Z
@Yahya yes exactly.
hanugm
CC BY-SA 4.0
2021-08-01T21:51:43.833Z
[Semi-supervised Generative Adversarial Networks Based on Scalable Support Vector Machines and Manifold Regularization](https://ieeexplore.ieee.org/document/9164865). Here the discriminator uses SVM classifier.
Yahya
CC BY-SA 4.0
2021-08-02T09:56:13.693Z
@Yahya Feel free to provide a formal answer below (as comments are temporary), if you have an answer to this question ;)
nbro
CC BY-SA 4.0
2021-08-02T16:44:21.257Z
Someone suggested to me to try using a Java compiler as a discriminator (to generate Java code), but I had to point out it wasn't differentiable and couldn't be weakened for training.
Criticize SE actions means ban
<generative-adversarial-networks>
Are there any Generative Adversarial Networks without Multi Layer Perceptrons?
hanugm
# Are there any Generative Adversarial Networks without Multi Layer Perceptrons?

(Asked by: username_0 on 2021-08-01)

Although the main stream research is on Generative Adversarial Networks(GANs) using Multi Layer Percepteons (MLPs). The original paper titled [Generative Adversarial Nets](https://arxiv.org/pdf/1406.2661.pdf) clealry says, in abstract, that GAN is possible with out MLP also

> In the **space of arbitrary functions** $G$ and $D$, a unique solution
> exists, with $G$ recovering the training data distribution and $D$
> equal to $\dfrac{1}{2}$ everywhere. In the case where G and D are defined
> by multilayer perceptrons, the entire system can be trained with backpropagation.

Are there any research papers that uses models other than MLPs and are comparatively successful?

**Tags:** generative-adversarial-networks

> **Comments:**
> **username_1**: [FCC-GAN: A Fully Connected and Convolutional Net Architecture for GANs](https://arxiv.org/pdf/1905.02417.pdf)  
> **username_0**: @username_1 It is also using MLP right?  
> **username_1**: I mix of two, but mainly a Convolution NN, look page 4, Table 1, the right column, for the architecture.  
> **username_0**: @username_1 Oh! In the question, i used MLP as a representative term for all forms of neural networks.  
> **username_1**: So Do you mean a non-ANN based model, such as a SVM or a simple Linear Model?  
> **username_0**: @username_1 yes exactly.  
> **username_1**: [Semi-supervised Generative Adversarial Networks Based on Scalable Support Vector Machines and Manifold Regularization](https://ieeexplore.ieee.org/document/9164865). Here the discriminator uses SVM classifier.  
> **username_2**: @username_1 Feel free to provide a formal answer below (as comments are temporary), if you have an answer to this question ;)  
> **username_3**: Someone suggested to me to try using a Java compiler as a discriminator (to generate Java code), but I had to point out it wasn't differentiable and couldn't be weakened for training.  

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## 0 answers

