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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.. | ||
data | ||
data_scripts | ||
metrics | ||
models | ||
options | ||
scripts | ||
temp | ||
utils | ||
requirements.txt | ||
run_scripts.sh | ||
test_Vid4_REDS4_with_GT_DUF.py | ||
test_Vid4_REDS4_with_GT_TOF.py | ||
test_Vid4_REDS4_with_GT.py | ||
test.py | ||
train.py |