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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.. | ||
__init__.py | ||
data_sampler.py | ||
Downsample_dataset.py | ||
LQ_dataset.py | ||
LQGT_dataset.py | ||
REDS_dataset.py | ||
util.py | ||
video_test_dataset.py | ||
Vimeo90K_dataset.py |