Due to its multi-scale nature, it is hard to translate the official implementation of Glow as a base distribution and a series of transformations. In particular, the shape and number of features change along the flow.
A first solution would be to define a MultiScaleNormalizingFlow distribution that would handle the removal of features in the log_prob method. Another solution would be to define a non-bijective transformation that would drop features in the forward direction and add random features in the inverse direction.
Description
Glow is multi-scale normalizing flow based on affine coupling transforms introduced in Glow: Generative Flow with Invertible 1x1 Convolutions (Kingma et al., 2018).
Implementation
Due to its multi-scale nature, it is hard to translate the official implementation of Glow as a base distribution and a series of transformations. In particular, the shape and number of features change along the flow.
A first solution would be to define a
MultiScaleNormalizingFlowdistribution that would handle the removal of features in thelog_probmethod. Another solution would be to define a non-bijective transformation that would drop features in the forward direction and add random features in the inverse direction.