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 <[email protected]> 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!" > [email protected] | - Train Engineer Assistant >
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