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Master thesis for the MSc. Artificial Intelligence at the University of Amsterdam, 2019. Topic: Super-resolution with Conditional Normalizing Flows.

Python 100.00%
conditional-normalizing-flows super-resolution probabilistic-generative-model

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cnfs-super-resolution's Issues

forward error!!!

In line 62 of optimization/trainer_baseline.py, the return values of model.forward(y, x) is z, x_bpd represents the latent code and minus log-det respectively according to class FlowModel in models/architectures/condNF.py. However, you receives the return values with logp_mass, _, _. It's an error obviously.

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