Experimenting with wgan notebook: I tried running the wgan notebook on another lsun category church_outdoor, for kicks. This is a smaller dataset (2.3GB), you can download any of the other 10 scene categories by replacing ‘category=bedroom’ with appropriate tag (church_outdoor for eg) in the notebook download instructions. To see improvements in GAN I’ve tried obvious things like a) showing more data to GAN and b) more iterations of the train loop. Other suggestions to improve the performance (visual appearance, rather) of the generated GAN’s are welcome!
PS: Found this guide on tips and tricks to make GANs work by Soumith, though its a year old and we’re doing most of it already (normalize data, use DCGAN, separate real and fake batches, leaky relu)
Increasing data sample size.
The images are for 10%, 50%, 100% respectively, of the church_outdoor dataset used (1 epoch).
Increasing training loops Running the notebook for 10, 50 and 250 iterations respectively with 100% data used. The images start looking more and more realistic.
Loss numbers for 10 iterations (6 min to run):
Loss_D [-1.37384]; Loss_G [0.72288]; D_real [-0.71672]; Loss_D_fake [0.65712]
For 250 iterations it took nearly 3 hours:
Loss_D [-0.50636]; Loss_G [0.45063]; D_real [-0.41054]; Loss_D_fake [0.09582]