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View Code? Open in Web Editor NEWCode for deep generative prior (ECCV2020 oral)
Home Page: https://arxiv.org/abs/2003.13659
License: MIT License
Code for deep generative prior (ECCV2020 oral)
Home Page: https://arxiv.org/abs/2003.13659
License: MIT License
In line 151 of models/dgp.py, you calculated and eventually added to total loss.
However, I cannot find any clue about nll loss in the paper.
Can you tell me why you calculated nll loss?
deep-generative-prior/models/dgp.py
Line 151 in 0a21d98
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.
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?
In sec 4.1 of the paper, BigGAN is trained with the c vector.
When generalize the DGP to the images which is not in ImageNet, how to choose a c vector?
Is the c vector fine-tuned in the training stage or fixed ?
Hi,
How can I use my own dataset for inpainting?
good works ! when we infer one image, it will finetune G, so one image it will cost more time ?
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.
Hi
can you tell me how to train my datasets?
What does it do to class?
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?
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!
Great work! The result is very impressive, by the way, have you do experiments on the human face datasets? I
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.
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?
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