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Publication: Controlling BigGAN Image Generation with a Segmentation Network

Experimental research project developed as part of Deep Learning Course requirement.

Contributors:
Mohamed Muzamil, Aman Jaiswal, Harpreet Singh Sodhi, Rajveen Singh Chandhok

The results are quite interesting as the model learnt to generate images in the required segment. Regularization and ensembling losses have given more accurate results but we are still exploring other techniques. Our next step is to include discriminator so that the images generated look more real. Generate a pull request if you want to conrtribute to the project.




Steps

  • Clone this repo.
  • To run the code, please download the pretrained pytorch weights first. Pretrained Weights
    biggan512-release.pt    # download this for generating 512*512 images
  • Upload the biggan512-release.pt file to your google drive.
  • Open ./Model.ipynb file in Google Colab or your Jupyter Notebook and run it. Comments are added to the file as needed.

References:

https://arxiv.org/abs/1809.11096
https://github.com/ivclab/BIGGAN-Generator-Pretrained-Pytorch
https://pytorch.org/hub/pytorch_vision_fcn_resnet101/
https://pytorch.org/hub/pytorch_vision_deeplabv3_resnet101/

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