GithubHelp home page GithubHelp logo

xingangpan / deep-generative-prior Goto Github PK

View Code? Open in Web Editor NEW
479.0 479.0 70.0 83.76 MB

Code for deep generative prior (ECCV2020 oral)

Home Page: https://arxiv.org/abs/2003.13659

License: MIT License

Python 91.31% Shell 8.69%
deep-learning gan generative-adversarial-network image-manipulation image-prior image-restoration

deep-generative-prior's People

Contributors

doubledaibo avatar xingangpan avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

deep-generative-prior's Issues

One model per example

Great work!

Just want to clarify one point. Since you choose to fine-tune the parameters of G by using this relaxed reconstruction loss, does it imply that for each example, we need one fine-tuned BigGAN for it? Thanks.

Pretrained BigGAN 512?

Hey! Thanks for the great work! I was wondering if you have a pretrained model with size 512 or a hint how the models are trained?

about one image speed ?

good works ! when we infer one image, it will finetune G, so one image it will cost more time ?

About G.eval()

Congratulations on your excellent work! In your code, I notice you set generator to eval() mode when you fine-tune the bigGAN, I wonder if it's necessary to set generator to eval() mode when we fine tune it.

Cannot reproduce the image reconstruction results (Table 1 in the paper).

Hi,
Thank you for your great work.
Could you share the training script for the image reconstruction experiment? I use the hyper-parameters of colorization for the image reconstruction experiment. But the results seem to be not good. On the ImageNet 1k val dataset, I only achieve psnr of 25.69 and ssim of 85.12, which are inferior to the results reported in the paper (psnr of 32.89, ssim of 95.95). Could you help me?

pretrained model 'D_256' seems to be corrupted

Thanks for publishing the code and pretrained model for the great paper!

I am not sure whether it is my problem when I download the model, but I got the size checking error for loading the pretrained model 'D_256'. However, when I switch to D_256_ch_64 and G_256_ch_64, the model loading problem disappear.

Thanks for your help!

Issues about switch off the update G

When I switch off the options --update_G, to perform the reconstruction, which means only optimize the latent code, the result seems far from satisfactory, even for imagenet val datasets.
image
In my understanding, optimizing just for latent code might not be perfect, but at least it shouldn't be this bad.
Is that consistent with what you tried?

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.