In natural images, transitions between adjacent pixels tend to be smooth and gradual, a fact that has long been exploited in image compression models based on predictive coding. In contrast, existing neural autoregressive image generation models predict the absolute pixel intensities at each position, which is a more challenging problem. In this paper, we propose to predict pixels relatively, by predicting new pixels relative to previously generated pixels (or pixels from the conditioning context, when available). We show that this form of prediction fare favorably to its absolute counterpart when used independently, but their coordination under an unified probabilistic model yields optimal performance, as the model learns to predict sharp transitions using the absolute predictor, while generating smooth transitions using the relative predictor. Experiments on multiple benchmarks for unconditional image generation, image colorization, and super-resolution indicate that our presented mechanism leads to improvements in terms of likelihood compared to the absolute prediction counterparts.
In this paper the authors present a new way to use autoregressive modeling to generate images pixel by pixel where each pixel is generated by modeling the difference between the current pixel value and  the preexistent ones. In order to achieve that, the authors propose a copy and adjustment mechanism that select an existing pixel, and then adjust its sub-pixel (channel values) to generate the new pixel. The proposed model is demonstrated with a suite of experiments in classic image generation benchmark. The authors also demonstrate the use of their technique in Image to Image translation.
Overall, although the paper explain clearly the intuition and the motivation of the proposed technique, I think that the paper in its present state have low novelty, weak related work analysis review and insufficient experiments to support a publication at ICLR. 
**Novelty, contribution and related work**
The authors should highlight better their main contribution novelty of the proposed method compared to their baseline.
**Result and conducted experiments**
the correctness of the proposed approach is not proved by the conducted experiment  in fact:
The experiments do not provide the details of the used architecture compared to your baseline. 
In Table 1 you report the results using your technique on several computer vision tasks (generation, colorization and super-resolution) but you're not comparing with the SoA of each of these tasks.
The  results reported in Tables 1 and 2 are not convincing  when compared to existing approaches (using only CIFAR10 in Table2). 
There are so many missing details specially to validate Image-To-image translation 
Figure 3 is confusing and  not clear  
**Minor comments**
In  references section : (Kingma & Dhariwal, 2018) is not in a proper format (nips 2018)
Bad quality of illustrations and images 
Be coherent with the position of captions (figure 3)