hindupuravinash / the-gan-zoo Goto Github PK

Hi,I want to do experiments on GAN based network traffic augmentation, but I don't know whether this model can be implemented and whether the code needs to be modified?

It would be great if you can add this new GAN
name: TrafficGAN
paper source: https://ieeexplore.ieee.org/abstract/document/8970742

Paper address：https://arxiv.org/abs/1905.01164
Source address：https://github.com/tamarott/SinGAN

Hi, dear,

I am thank you for the zoo. however, I can't seize some keywords I need when read the zoo to search a paper. Could you group it by years or category like 3D, image, etc.

My enligsh is poor and you can get my mean?

Thank you!

AttGAN code has been released recently. https://github.com/LynnHo/AttGAN-Tensorflow

Thank you very much

Hello everyone,
I am working with infrared images about the objects in the sea for example boats, Can anyone suggest me a better GAN from the list that useful for generating new infrared images?

Note: GAN that also contains code and also support customized datasets

Thank you very much for your cooperation and time.

It would be super useful if you would provide links to corresponding github projects with implementation as well.

The script timeline.py is really useful in updating the cumulative distribution of GAN papers over time. However, I prefer not to launch it when adding new GANs because the size of the generated image differs from the original one whether I keep the original plot size or I put it in full screen.
Setting explicitly the size with plt.figure(figsize=..)) and saving the image to file with plt.savefig(..) would standardize this step.

Impossible to read all of the titles...

Hey, this is super useful, but in a quick glance if I'm looking for something that came out only in (let's say), the last 2 years, it'd be more informative to add the years of release next to the names as well!
But an awesome job with this!

My problem is that I want to train a network to do the following: input a set of parameters and output a image. which gans can i use or at least refer to. anybody can help me?

I just noticed that there is a github link available for ALI (Adversarially Learned Inference): https://github.com/IshmaelBelghazi/ALI

Kernel GANs - https://arxiv.org/abs/1705.09199

Generating Images with Perceptual Similarity Metrics based on Deep Networks. Maybe call it "DeePSiM" based on the paper?

DEEP MULTI-SCALE VIDEO PREDICTION BEYOND MEAN SQUARE ERROR. I can't come up with a proper abbreviation. : )

UNSUPERVISED LEARNING OF VISUAL STRUCTURE USING PREDICTIVE GENERATIVE NETWORKS. Maybe call it "PGN"?

Hello! I'd like to do stain unmixing with deep leaning. My idea is that the input is a mixed image and the output is three images with only one colour. Can I realize it with DCGAN?

ICCV 19
Paper title: Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization
Paper: https://arxiv.org/abs/1908.06965
Github: https://github.com/mahfuzmohammad/Fixed-Point-GAN

So many papers about gan. I am a fresh man, I do not know which paper I should begin to read and learn. Anyone else could give some suggestion? Thanks.

It would be nice to have more information about models as date, author, underlying platform (Tensorflow, Pytorch, etc.), number of layer, etc.

A-SRGAN: is an attentional Super-resolution GAN applied to create HD images.

1. https://arxiv.org/abs/1812.04821

Step-Up GAN is a data continuation approach which was part of these works:

1. https://digitalcommons.wpi.edu/etd-theses/1256/
2. https://www.wpi.edu/news/announcements/data-science-ms-thesis-presentation-xiaozhou-zou

https://github.com/pfnet-research/chainer-gan-lib
https://drive.google.com/file/d/0B8HZ50DPgR3eSVV6YlF3XzQxSjQ/view

Hi, Thx for this job, but I find that the link of SSGAN should be "https://arxiv.org/abs/1707.01613", may be you should fix this.

The RNN-WGAN's repo link https://github.com/liuyuemaicha/Adversarial-Learning-for-Neural-Dialogue-Generation-in-Tensorflow is wrong. The true link is https://github.com/amirbar/rnn.wgan, which is owned by the author of the paper.

The "Adversarial Learning for Neural Dialogue Generation" is another paper using GAN idea, which has not been included in gan-zoo. The paper's link is https://arxiv.org/abs/1701.06547. The official repo is https://github.com/jiweil/Neural-Dialogue-Generation.

https://github.com/sindresorhus/awesome if you don't know what i'm talking about, seems like this would be quite fitting if reformatted right

Hello, I would like to contribute to add a column in https://github.com/hindupuravinash/the-gan-zoo/blob/master/gans.tsv to access to implement of that particular GAN.

Hi,I want to do experiments on GAN neural machine translation, but I don't know whether this model can be implemented and whether the code needs to be modified?
I am a beginner in NMT. Hope to give answers or suggestions.That would be really help!

Hi @hindupuravinash, I noticed that for Conditional GANs (CGANs) a recent work is reported (https://arxiv.org/abs/1703.06029), but the method was already introduced in 2014 (https://arxiv.org/abs/1411.1784).
If you check the references in arXiv:1703.06029, you see the latter cited as previous work.

hi, @hindupuravinash

Thanks for you contribution to the community, I found this paper is high cited and impactful in areas of applying GANs for domain adaptation tasks :)

http://openaccess.thecvf.com/content_cvpr_2017/html/Bousmalis_Unsupervised_Pixel-Level_Domain_CVPR_2017_paper.html

https://arxiv.org/abs/1611.02163

I am a freshman for GAN and want to learn it. Can someone tell me which GAN is the best at present? Thank you. O(∩_∩)O~~

https://arxiv.org/abs/1703.10239

It'd be great if you could add this one as well.

Tempered Adversarial Networks
https://arxiv.org/abs/1802.04374

Not sure if it qualifies, since it doesn't have any cool abbreviation in the title :)

The website refered in the README.MD Deep Hunt does not work.

The link skip to a URL named https://medium.com/m/global-identity?redirectUrl=https%3A%2F%2Fdeephunt.in%2F

Is the URL changed? What is the new one?