forked from mrq/DL-Art-School
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 | ||