  Generative flows models enjoy the properties of tractable exact likelihood
and efficient sampling, which are composed of a sequence of invertible
functions. In this paper, we incorporate matrix exponential into generative
flows. Matrix exponential is a map from matrices to invertible matrices, this
property is suitable for generative flows. Based on matrix exponential, we
propose matrix exponential coupling layers that are a general case of affine
coupling layers and matrix exponential invertible 1 x 1 convolutions that do
not collapse during training. And we modify the networks architecture to make
trainingstable andsignificantly speed up the training process. Our experiments
show that our model achieves great performance on density estimation amongst
generative flows models.
