[mlpack] Essential GAN variants for GSoC

[Sidakpal Singh]

Hi Ryan,

Can you please review my proposal that I had prepared for GSoC?

Thank you so much. ​
 mlpack GSoC proposal
<https://docs.google.com/document/d/1UpauuhdmHG_keE8RkTrxWit59l0i15-ry0FaQgiK_j8/edit?usp=drive_web>
​

Best,
Sidak

http://sidakpal.com/

On 28 March 2017 at 19:55, Ryan Curtin <ryan at ratml.org> wrote:

> On Tue, Mar 28, 2017 at 03:41:34AM +0530, Sidakpal Singh wrote:
> > Hi,
> >
> > I am a final year CS undergraduate at Indian Institute of Technology
> (*IIT*)
> > Roorkee and will be joining *Microsoft Research* later this fall. I am
> > interested in being a part of GSoC with mlpack and contribute to the*
> > Essential Deep Learning Modules* project.
> >
> > I believe that Generative Adversarial Networks are an extremely hot topic
> > of research in Deep learning these days. So, it would be immensely useful
> > if we are able to provide an API of the major types of GANs. Here's what
> I
> > have in mind.
> >
> > 1. The *vanilla GAN*
> > 2. Generative Moment Matching Networks (*GMMN*)
> > 3. Deep Convolutional Generative Adversarial Nets (*DCGAN*)
> > 4. *Conditional GANs* (Basic, StackGANs, Plug and Play Generative
> Networks)
> > 5. *InfoGAN* (encode meaningful features in the noise Z)
> > 6. *Wasserstein *GAN & f-GAN (*Divergence minimization*)
> >
> > The above are the most commonly used variants of GANs and the ones
> against
> > which
> > researchers *generally compare* their variant of GANs. Since this will
> > broadly cover the major
> > GAN types, it would allow for *easy extensibility* when somebody wants to
> > design their own variant. Further, as RBMs take much *more time for
> > sampling*, providing these variants might be sufficient. Lastly, I am
> open to
> > *discussing other models* we may include which I might have missed and
> are
> > important.
> >
> > Btw, if time permits, we may also include *Variational & Adversarial
> > Autoencoders* or *PixelRNN* and some applications like image to image
> > translation, inpainting etc.
> >
> > *Background: *Since last summer I have been working on *formulating a new
> > loss function for training GANs* based on Optimal Transport distances.
> This
> > work started off via my internship at *Kyoto University*, Japan with
> *Prof.
> > Marco Cuturi*. This has given me a great experience in dealing with the
> > instability issues involved in training GANs. Further, I have a very
> solid
> > understanding of *Wasserstein distances* which are essentially a kind of
> > Optimal transport distances. Also, here is a link to an implementation of
> > GAN in Chainer, which I built for playing around with them.
> >
> > https://github.com/sidak/GAN-Chainer
> >
> > I have a strong background in ML through courses and projects, and have
> > also used TensorFlow and  Shogun. Besides, I have also carried out
> research
> > at *Xerox* and *Purdue University*, USA and my work has also been
> published
> > in *IJCAI*.
> >
> > It would be really great if you can *share your views* on this and
> suggest
> > if this would *serve as a *
> > *good plan* for the GSoC. Also, it will be really wonderful if you can
> > guide me on to the next steps. :)
>
> Hi Sidak,
>
> Thanks for getting in touch.  I think that implementing GANs and related
> techniques could be an interesting and exciting summer project.  If
> you're planning to prepare a proposal on that, I would suggest that you
> spend a good amount of time with the mlpack ANN codebase so that you can
> detail, in your proposal, how the different GAN modules will be
> implemented.  It's really important for us, when we evaluate proposals,
> to be able to see how well a proposed piece of code will fit into the
> rest of the library.
>
> I hope that this is helpful; let me know if I can clarify anything.
>
> Thanks,
>
> Ryan
>
> --
> Ryan Curtin    | "Get out of the way!"
> ryan at ratml.org |   - Train Engineer Assistant
>
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