Did you have a look at https://github.com/layumi/2016_GAN_Matlab
Matlab does not currently support Generative Adversarial Neural Networks
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    ashkan dashtban
 el 21 de Abr. de 2018
  
    
    
    
    
    Respondida: anbarasi l
 el 30 de Nov. de 2022
            I searched a lot to see if Matlab supports GAN but unfortunately it does not. I just found deconvolution layer. does anybody know how I can use that for designing a GAN. I am gonna integrate a GAN block within a DAG network and I am not sure if it is possible. I found also another open source lib 'MatConvNet'. is is possible to use it within DAG? Thank you very much I hope it is not wasting time working on deep learning with Matlab.
web address of DAG : https://blogs.mathworks.com/deep-learning/2018/03/19/creating-a-dag-network-from-dag-parts/
9 comentarios
  Walter Roberson
      
      
 el 18 de Jul. de 2018
				ashkan dashtban comments to me:
Exactly and we hopefuly expect it to be available in near future!
  Kenta
      
 el 15 de Mayo de 2020
				As of now, some kinds of GAN such as conditional GAN are available. 
For example, https://jp.mathworks.com/matlabcentral/fileexchange/75441-conditional-gan-and-cnn-classification-with-fashion-mnist

Respuesta aceptada
  Yui Chun Leung
 el 4 de Abr. de 2020
        I implemented different types of GANs with Matlab, including DCGAN, cGAN, CycleGAN and more. 
2 comentarios
  Atallah Baydoun
 el 4 de Abr. de 2020
				Thanks Yui,
I will take a look and see if I can adapt pix2pix to read .mat files instead of jpg and png.
Atallah
  Yui Chun Leung
 el 4 de Abr. de 2020
				Atallah,
Here is the code used for transforming jpg and png to mat.
https://github.com/zcemycl/Matlab-GAN/blob/master/CycleGAN/LoadAppleOrangeDatabase.m https://github.com/zcemycl/Matlab-GAN/blob/master/Pix2Pix/LoadFacadeDatabase.m
But if you want a dataloader like in pytorch, you have to rewrite the for loop and put the jpg loader in it.
Más respuestas (6)
  Shounak Mitra
    
 el 6 de Jun. de 2019
        In the next release, we're going to provide some flexbilities to design advanced networks such as GANs, Siamese Networks etc. Hang on :)
2 comentarios
  KH TOHIDUL ISLAM
      
 el 19 de Sept. de 2019
        It is avilabel now, please check this out. Train Generative Adversarial Network (GAN)
2 comentarios
  Atallah Baydoun
 el 19 de Sept. de 2019
				Yep, It was the first thing I checked when I got the email about Matlab 2019 b.
Thanks!
  Atallah Baydoun
 el 7 de Jun. de 2019
        Thanks Mitra for the info.
But when is the next release going to be ?
5 comentarios
  Shounak Mitra
    
 el 10 de Jun. de 2019
				Let's think of it in terms of what important features you need to create GANs - autodifferentiation, custom training loops, and may be custom layers too (if you're curious!). All those things are planned to be supported. And of course, the deep learning layer support will be there.
  Atallah Baydoun
 el 10 de Jun. de 2019
				Hopefully then Matlab will include an example on GAN generation also.
  Wonho Song
 el 28 de Mayo de 2020
        Hello.
Thanks for these nice codes.
Normally we do have checkpoints and model to save to a file.
- I want to know how to save the model.Is it good to save the paramGen and paramDis? and if it is then how to use as a model.
- 2 .And also I want to know how to test pix2pix.m to a specific image file.Thanks in advance.
1 comentario
  Atallah Baydoun
 el 28 de Mayo de 2020
				Hey Wonho,
I ended up by using pix2pix from this link.
https://www.mathworks.com/matlabcentral/fileexchange/74779-pix2pix
It automatically saves the model. You can se from the link that many troubleshooting issues were resolved.
  David Willingham
    
 el 27 de Ag. de 2020
        Since R2019b, MATLAB has supported the implementation of GAN's. 
Regards,
David Willingham
Deep Learning Product Manager, MathWorks
0 comentarios
  anbarasi l
 el 30 de Nov. de 2022
        MATLAB has supported the implementation of CGAN.  
You can find information on this in the below link
Train Conditional Generative Adversarial Network (CGAN)
https://in.mathworks.com/help/deeplearning/ug/train-conditional-generative-adversarial-network.html
0 comentarios
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