In recent years we have witnessed tremendous progress in unpaired image-toimage translation methods, propelled by the emergence of DNNs and adversarial training strategies. However, most existing methods focus on transfer of style and appearance, rather than on shape translation. The latter task is challenging, due to its intricate non-local nature, which calls for additional supervision. We mitigate this by descending the deep layers of a pre-trained network, where the deep features contain more semantics, and applying the translation between these deep features. Specifically, we leverage VGG, which is a classification network, pre-trained on ImageNet with large-scale semantic supervision. Our translation is performed in a cascaded, deep-to-shallow, fashion, along the deep feature hierarchy: we first translate between the deepest layers that encode the higher-level semantic content of the image, proceeding to translate the shallower layers, conditioned on the deeper ones. We show that our method is able to translate between different domains, which exhibit significantly different shapes. We evaluate our method both qualitatively and quantitatively and compare it to state-of-the-art image-to-image translation methods. Our code and trained models will be made available.
* Summarize what the paper claims to do/contribute.
This paper claims to extend existing image translation works, like CycleGAN, to domain pairs that are not similar in shape. It is proposed to do so by using a VGG network trained on classification (I assume on Imagenet), extracting features from the two domains and learn 5 CycleGANs to translate for each level of the feature hierarchy. At each level of the hierarchy the translation from the previous level is used to condition the translation for the current level. During inference, the final image translation is done by "feature inversion" (a technique proposed in Dosovitsikiy and Brox, 2016) from the final feature layer. The technique is show on example from a number of pairs of domains like Zebra-to-Elephant (and back), Giraffe-to-Zebra (and back), Dog-to-Cat (and back) and is compared with a number of baselines qualitatively and quantitatively with the FID score.  
* Clearly state your decision (accept or reject) with one or two key reasons for this choice.
Weak Reject.
Major reasons:
- The problem itself, as stated in the introduction, seems ill-posed to me. One of the struggles I had while looking through the results was to understand what the images should be looking like. ie What should a zebra translated to a giraffe look like? The motivation for such a problem is also not immediately clear either. 
- Most of the resulting images do not seem "translated" to me. As stated in the paper (end of p.2) "one aims to transform a specific type of object without changing the background." As one can see in eg Fig. 1 the resulting translations are completely different images with the foreground object of the new domain in roughly similar poses. The background in most cases does not persist. What I suspect is actually happening here is that the high-level semantics from the first image are used as some sort of noise to generate new images from the new domain. One question I had, for example: could we be getting similar results if we used the VGG bottleneck as the noise vector in an InfoGAN? Since the VGG network is pretrained and used in the same way in both domains, I imagine we would be seeing something very similar. (and it would be def. preferrable to tuning 10 GANs!)
* Provide supporting arguments for the reasons for the decision.
Some of the decisions made in the paper were unclear and not supported adequately. The questions (in rough order of importance) that made some of the contributions unclear to me:
- Why wasn't a final translator used for the final image, conditioned on the final \tilde{b}_1? 
- Is the VGG network pretrained on ImageNet? Why wasn't another task used that could be retaining more of the relevant features? eg on semantic segmentation
- Could this be used for networks pretrained on other datasets? Presumably ImageNet has information about the animals translated in this paper. Even better, could we somehow learn these features for the domain pairs automatically somehow?
- How meaningful is the FID score really in this case?
- How were the 10 GANs tuned?
* Provide additional feedback with the aim to improve the paper. Make it clear that these points are here to help, and not necessarily part of your decision assessment.
- It is mentioned on p.4 that "clamping is potentially a harmful irreversible operation" but that harmful results were not observed. As I was reading that I was wondering how these results would actually look like. 
- On p. 6 it is mentioned that the number of images for 2 categories are reported in another paper. I think it'd take less space to actually report the number of images here.
- On p.7 it is mentioned that the number of instances is preserved, however it should be made clear that it's is perserved in some (or most if that is what was observed) of the examples.