Paper:
Published as a conference paper at ICLR 2018 PIXELNN: EXAMPLE-BASED IMAGE SYNTHESIS
We present a simple nearest-neighbor (NN) approach that synthesizes highfrequency photorealistic images from an "incomplete" signal such as a lowresolution image, a surface normal map, or edges. Current state-of-the-art deep generative models designed for such conditional image synthesis lack two important things: (1) they are unable to generate a large set of diverse outputs, due to the mode collapse problem. (2) they are not interpretable, making it difficult to control the synthesized output. We demonstrate that NN approaches potentially address such limitations, but suffer in accuracy on small datasets. We design a simple pipeline that combines the best of both worlds: the first stage uses a convolutional neural network (CNN) to map the input to a (overly-smoothed) image, and the second stage uses a pixel-wise nearest neighbor method to map the smoothed output to multiple high-quality, high-frequency outputs in a controllable manner. Importantly, pixel-wise matching allows our method to compose novel high-frequency content by cutting-and-pasting pixels from different training exemplars. We demonstrate our approach for various input modalities, and for various domains ranging from human faces, pets, shoes, and handbags.

Review:
This paper presents a pixel-matching based approach to synthesizing RGB images from input edge or normal maps. The approach is compared to Isola et al’s conditional adversarial networks, and unlike the conditional GAN, is able to produce a diverse set of outputs.
Overall, the paper describes a computer visions system based on synthesizing images, and not necessarily a new theoretical framework to compete with GANs. With the current focus of the paper being the proposed system, it is interesting to the computer vision community. However, if one views the paper in a different light, namely showing some “blind-spots” of current conditional GAN approaches like lack of diversity, then it can be of much more interest to the broader ICLR community.
Pros: 
Overall the paper is well-written
Makes a strong case that random noise injection inside conditional GANs does not produce enough diversity
Shows a number of qualitative and quantitative results
Concerns about the paper:
1.) It is not clear how well the proposed approach works with CNN architectures other than PixelNet
2.) Since the paper used “the pre-trained PixelNet to extract surface normal and edge maps” for ground-truth generation, it is not clear whether the approach will work as well when the input is a ground-truth semantic segmentation map.
3.) Since the paper describes a computer-vision image synthesis system and not a new theoretical result, I believe reporting the actual run-time of the system will make the paper stronger. Can PixelNN run in real-time? How does the timing compare to Isola et al’s Conditional GAN?
Minor comments:
1.) The paper mentions making predictions from “incomplete” input several times, but in all experiments, the input is an edge map, normal map, or low-resolution image. When reading the manuscript the first time, I was expecting experiments on images that have regions that are visible and regions that are masked out. However, I am not sure if the confusion is solely mine, or shared with other readers.
2.) Equation 1 contains the norm operator twice, and the first norm has no subscript, while the second one has an l_2 subscript. I would expect the notation style to be consistent within a single equation (i.e., use ||w||_2^2, ||w||^2, or ||w||_{l_2}^2)
3.) Table 1 has two sub-tables: left and right. The sub-tables have the AP column in different places.
4.) “Dense pixel-level correspondences” are discussed but not evaluated.