44b89330c2
This is a checkpoint of a set of long tests with reduced-complexity networks. Some takeaways: 1) A full GAN using the resnet discriminator does appear to converge, but the quality is capped. 2) Likewise, a combination GAN/feature loss does not converge. The feature loss is optimized but the model appears unable to fight the discriminator, so the G-loss steadily increases. Going forwards, I want to try some bigger models. In particular, I want to change the generator to increase complexity and capacity. I also want to add skip connections between the disc and generator. |
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.. | ||
archs | ||
__init__.py | ||
base_model.py | ||
loss.py | ||
lr_scheduler.py | ||
networks.py | ||
SR_model.py | ||
SRGAN_model.py | ||
Video_base_model.py |