Comments (1)
Hi @prclibo,
for the paper Resampling Base Distributions of Normalizing Flows I created a separate repository. You can use the script train_glow.py
together with the configuration file gauss_32.yaml
if you want to use a Gaussian base distribution or resampled_32.yaml
if you want to use the resampled base distribution we introduced in the paper. You should get bpd values of less then 3.3 but notice that depending on the hardware you use this might take several days.
Let me if you still have problems reproducing the results.
from normalizing-flows.
Related Issues (20)
- Example usage for images HOT 2
- More functionality HOT 2
- Putting examples in the documentation HOT 5
- Forward and Inverse with log det function for `MultiscaleFlow`
- multi-gpu implementation HOT 1
- How the inverse was calculated HOT 1
- Conditional Flows implementation / documentation HOT 2
- Remove Lambda's HOT 6
- Negative KL divergence HOT 3
- issue about ConditionalNormalizingFlow HOT 2
- The original glow seems to use `ConditionalDiagGaussian` HOT 1
- exp and sigmoid may cause inf. HOT 3
- Could you give an example for NICE? HOT 1
- NICE demo? HOT 1
- What dou you mean by "Augmented Normalizing Flow based on Real NVP"? HOT 1
- one-dimensional coupling flows do not work HOT 3
- Seeking Advice on Designing an Invertible Neural Network for Fission HOT 2
- Calculating forward KL divergence (probability density maximization), I get negative loss results on my dataset, is this reasonable? HOT 1
- Cannot have an odd latent_size (working with 2, 4, etc. , but not 3 or 5), shape problem HOT 2
- Conditional Coupling Layers
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from normalizing-flows.