Paper:
Flow-based generative models are powerful exact likelihood models with efficient sampling and inference. Despite their computational efficiency, flow-based models generally have much worse density modeling performance compared to stateof-the-art autoregressive models. In this paper, we investigate and improve upon three limiting design choices employed by flow-based models in prior work: the use of uniform noise for dequantization, the use of inexpressive affine flows, and the use of purely convolutional conditioning networks in coupling layers. Based on our findings, we propose Flow++, a new flow-based model that is now the state-of-the-art non-autoregressive model for unconditional density estimation on standard image benchmarks. Our work has begun to close the significant performance gap that has so far existed between autoregressive models and flow-based models.

Review:
I think the ideas are of sufficient interest to the community to merit acceptance & discussion, but I still miss the high resolution samples we got with the Glow paper. Responses to my concerns somewhat addressed, though simpler alternatives to uniform dequant would be nice.
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Improvements are attained on two image datasets by (a) variational dequantization, (b) mixture CDF coupling layers, and (c) self-attention in conditioning net.
Quality: The work is fine, demonstrating familiarity with recent work in flows and improving upon it. The experiments are on CIFAR-10 and 32x32 ImageNet. Unclear if the evaluation numbers are on a test set or a 'validation' set. I will be assuming test set. The visualizations are fine, but not nearly as convincing as the Glow visualizations on CelebA.
Clarity: The presentation is clear enough, and the motivation seems reasonable, though the assertion that all AR models are slow seems a bit belied by the recent WaveRNN work, which gets a Wavenet like model running in realtime on a phone. On the other hand, I felt like the proposed fixes were all a bit scattered here & there. Each could stand as a research topic on its own, and one paper can't fit in much analysis of all three. For example, a RealNVP style model usually needs to shuffle or reverse the channels to attain decent performance, but there's no discussion of how/whether that is done here. Folks wanting to replicate this work would want a formula for the tractable log-abs-det-jacobian of the coupling layer, but all we have is "involves calculating the pdf of the logistic mixtures".
Originality: Self-attention is not new, though its uptake in the conditioning networks of flow models has been slow/nonexistent. I found the dequantization improvement more novel. The new proposal for a coupling layer seems like a clever way of introducing more parameters in a structured manner. 
Significance: Bringing flow models closer to the performance of AR models is good progress.
Questions
I wonder whether some kind of spline or cubic interpolation might achieve similar improvement over the uniform dequantization. Perhaps uniform is not the best baseline?
The new coupling layer might just be viewed as a way of introducing many more parameters in a structured manner. Have you compared parameter counts?
Appendix B shows some portion of the code, but seems like a missed opportunity to fit this into a framework like tfp.bijectors. The code seems glued in somewhat slapdash. For example, the tf_go function looks like debugging/logging code (unwanted), and lacks any usage.
I think this work is promising and interesting to the probabilistic modeling community, but needs some cleanup and some more compelling presentation (non image data? Glow-style graphics?).