CONTEXT-ADAPTIVE ENTROPY MODEL FOR END-TO- END OPTIMIZED IMAGE COMPRESSION
We propose a context-adaptive entropy model for use in end-to-end optimized image compression. Our model exploits two types of contexts, bit-consuming contexts and bit-free contexts, distinguished based upon whether additional bit allocation is required. Based on these contexts, we allow the model to more accurately estimate the distribution of each latent representation with a more generalized form of the approximation models, which accordingly leads to an enhanced compression performance. Based on the experimental results, the proposed method outperforms the traditional image codecs, such as BPG and JPEG2000, as well as other previous artificial-neural-network (ANN) based approaches, in terms of the peak signal-to-noise ratio (PSNR) and multi-scale structural similarity (MS-SSIM) index. The test code is publicly available at https://github.com/JooyoungLeeETRI/CA_Entropy_Model.
Update:
I have updated my review to mention that we should accept this work as being concurrent with the two papers that are discussed below.
Original review:
This paper is very similar to two previously published papers (as pointed by David Minnen before the review period was opened):
"Learning a Code-Space Predictor by Exploiting Intra-Image-Dependencies" (Klopp et al.)  from BMVC 2018,
and
"Joint Autoregressive and Hierarchical Priors for Learned Image Compression" (Minnen et al.) from NIPS 2018.
The authors have already tried to address these similarities and have provided a list in their reply, and my summary of the differences is as follows (dear authors: please comment if I am misrepresenting what you said):
(1) the context model is slightly different
(2) parametric model for hyperprior vs non-parametric
(3) this point is highly debatable to be considered as a difference because the distinction between using noisy outputs vs quantized outputs is a very tiny detail (any any practitioner would probably try both and test which works better). 
(4) this is not really a difference. The fact that you provide details about the method should be a default! I want all the papers I read to have enough details to be able to implement them.
(5+)  not relevant for the discussion here.
If the results were significantly different from previous work, these differences would indeed be interesting to discuss, but they didn't seem to change much vs. previously published work.
If the other papers didn't exist, this would be an excellent paper on its own. However, I think the overlap is definitely there and as you can see from the summary above, it's not really clear to me whether this should be an ICLR paper or not. I am on the fence because I would expect more from a paper to be accepted to this venue (i.e., more than an incremental update to an existing set of models, which have already been covered in two papers).