This repository contains Pytorch implementation of experiments from the paper Implicit Normalizing Flows. The implementation is based on Residual Flows.
Implicit Normalizing Flows generalize normalizing flows by allowing the invertible mapping to be implicitly defined by the roots of an equation F(z, x) = 0. Building on Residual Flows, we propose:
- A unique and invertible mapping defined by an equation of the latent variable and the observed variable.
- A more powerful function space than Residual Flows, which relaxing the Lipschitz constraints of Residual Flows.
- A scalable algorithm for generative modeling.
As an example, in 1-D case, the function space of ImpFlows contains all strongly monotonic differentiable functions, i.e.
A 1-D function fitting example:
- PyTorch 1.4, torchvision 0.5
- Python 3.6+
NOTE: By default, O(1)-memory gradients are enabled. However, the logged bits/dim during training will not be an actual estimate of bits/dim but whatever scalar was used to generate the unbiased gradients. If you want to check the actual bits/dim for training (and have sufficient GPU memory), set --neumann-grad=False. Note however that the memory cost can stochastically vary during training if this flag is False.
Toy 2-D data:
bash run_toy.sh
CIFAR10:
bash run_cifar10.sh
Tabular Data:
bash run_tabular.sh
To be done after ICLR 2021.