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val-iisc / deligan

This project is an implementation of the Generative Adversarial Network proposed in our CVPR 2017 paper - DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data. DeLiGAN is a simple but effective modification of the GAN framework and aims to improve performance on datasets which are diverse yet small in size.
MIT License
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Some questions about the project #2

Closed OwalnutO closed 6 years ago

OwalnutO commented 6 years ago

Two questions: 1.Do you find performance reduction if you increase the size of the generated image? Such as the dataset of the sketch? Can you give some suggestions if I want to increase the size of the results? 2.How to modify the network if I want to generate specific kind of images, just like the conditional GAN? Any suggestions? Thanks!

swami1995 commented 6 years ago

We didn't really perform any experiments with increased image resolution. But my intuition says simply increasing the image size would result in performance deterioration. I think you'd have to correspondingly increase the dataset size. Also, I think it'll require some hyperparameter tuning. But I think you should observe better performance once you make these changes. In order to get conditional generation in a DeliGAN setting, just take any conditional GAN framework, and sample from the mixture of Gaussian setup as in DeliGAN, instead of using a unit normal distribution . This was the point of DeliGAN. View it more like a layer that can be plugged into any other architecture to learn a more flexible prior distribution for the generator. You could also tweak the DeliGAN model we have provided to emulate the conditional GAN setup, but that would be more cumbersome.