I'm trying to generate images at minimum of size 128 x 128 with a Generative Adversarial Network. I already tried a SAGAN pytorch implementation, but I'm not very happy with results. The images look cool but and I see some correct shape but without explanation you wouldn't know what the images are about. I have a dataset of 4000 images. Lightness, colors and shapes vary a lot, but they are similar in style and on what they portray.
- With a Google Cloud V100 GPU the GAN would run a week to two with default parameters. Does this sound realistic time for this kind of dataset? It's definitely not feasible for me.
- Is 4000 images enough to train a GAN from scratch?
- Is there any implementation with pytorch/keras that would be good to get nice results with?