Body: <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>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-01T21:24:35.170Z
Comments.Text: [FCC-GAN: A Fully Connected and Convolutional Net
  Architecture for GANs](https://arxiv.org/pdf/1905.02417.pdf)
Comments.UserDisplayName: Yahya
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-01T21:32:38.663Z
Comments.Text: @Yahya It is also using MLP right?
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-01T21:34:24.570Z
Comments.Text: I mix of two, but mainly a Convolution NN, look page 4, Table 1, the right column, for the architecture.
Comments.UserDisplayName: Yahya
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-01T21:43:54.147Z
Comments.Text: @Yahya Oh! In the question, i used MLP as a representative term for all forms of neural networks.
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-01T21:45:11.670Z
Comments.Text: So Do you mean a non-ANN based model, such as a SVM or a simple Linear Model?
Comments.UserDisplayName: Yahya
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-01T21:47:04.040Z
Comments.Text: @Yahya yes exactly.
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-01T21:51:43.833Z
Comments.Text: [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.
Comments.UserDisplayName: Yahya
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-02T09:56:13.693Z
Comments.Text: @Yahya Feel free to provide a formal answer below (as comments are temporary), if you have an answer to this question ;)
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-02T16:44:21.257Z
Comments.Text: 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.
Comments.UserDisplayName: Criticize SE actions means ban
Tags: <generative-adversarial-networks>
Title: Are there any Generative Adversarial Networks without Multi Layer Perceptrons?
UserDisplayName: hanugm
ThreadText: # 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**: @Yahya 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**: @Yahya 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**: @Yahya 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**: @Yahya 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.  
  
  ---
  
  ## 0 answers
  
  