Hi, Marcus Thanks for your reply. Sorry for forget to add the cc to mailing list in last email.
I think adding another model besides WGAN or SGAN would fulfill that > requirement. What do you think which model we can add besides WGAN or SGAN? Do you mean that add a model associate with WGAN or SGAN? what's more, do you think that it is feasible to implement both the WGAN and SGAN separately? Thanks 2017-03-19 23:32 GMT+08:00 Marcus Edel <[email protected]>: > Hello YuLun > > welcome and thanks for getting in touch! > > I think the WGAN is wonderful, so I want to implement it too. and I'm > wonder > that is it full enough for three month's work to just implement one module > between SGAN and WGAN? but when I want to integrate two modules I found > there is > not much in common between them. So I'm not sure what should I do. Can you > give > me some advice and guide me what should I do next? > > > It is a really great idea and well written paper. Regarding if > implementing a > single model SGAN or WGAN is enough work for GSoC, I don't think so, even > if you > like to implement a bunch of different test scenarios. I think adding > another > model besides WGAN or SGAN would fulfill that requirement. What do you > think? > > Thanks, > Marcus > > On 19 Mar 2017, at 08:40, YuLun Cai <[email protected]> wrote: > > Hello, > I am YuLun Cai from China. I am currently in my first year of Master > studies. I am interested in participating inGSoC 17 with mlpack in Essential > Deep Learning Modules. > > Among the topics given on the wiki page, I am interested in implemening > GAN modules. I have done a course in Advance Machine Learning and I've > finished the Stanford course "CS231n: Convolutional Neural Networks for > Visual Recognition" for self-study, which help me a lot in understand the > deep learning. > > I've built the mlpack from source in my own machine successfully, then I > look at the source code in the ANN module(the activation_functions, lots of > layers and the api in ffn.hpp and rnn.hpp to learn how to build a neural > network in mlpack) . > > I also learn to resource about GAN in the GSOC project wiki, I think the > "Stacked Generative Adversarial Networks"[1] is interesting, which consists > of a top-down stack of GANs and try to invert the hierarchical > representations of a discriminative bottom-up deep network to generate images. > > In addition, recently the Wasserstein GAN paper[2] gets a lot of > attention, many people think it is excellent: > * it proposes a new GAN training algorithm that works well on the common > GAN datasets > * there is just a little difference between the original GAN and WGAN > algorithm > * its training algorithm is backed up by theory. it clarifies that the > original GAN sometimes doesn't provide gradient to train when using KL > divergence or JS divergence, and prove that through the Wasserstein distance > the gradient always can be provided. > * In the Wasserstein GAN, it can train the discriminator to convergence > and also can improve the stability of learning, get rid of the mode collapse. > > I think the WGAN is wonderful, so I want to implement it too. and I'm > wonder that is it full enough for three month's work to just implement one > module between SGAN and WGAN? but when I want to integrate two modules I > found there is not much in common between them. So I'm not sure what should I > do. Can you give me some advice and guide me what should I do next? > Thanks > > [1] https://arxiv.org/abs/1612.04357 > [2] https://arxiv.org/abs/1701.07875 > > _______________________________________________ > mlpack mailing list > [email protected] > http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack > > >
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