Top and Bottom Right: RealNVP [3] uses checkerboard and channel-wise partitioning schemes in order to factor out parameters and ensure that there aren’t redundant partitions from previous layers. GLOW [4] uses an invertible 1×1 convolution which allows the partitioned to be ‘learned’ by a linear layer. We show that arbitrary partitions can be simulated in a constant number of layers with a fixed partition, showing that these ideas increase representational power by at most a constant factor. Bottom Left: Random…
