Paper:
Published as a conference paper at ICLR 2018 SIMULATED+UNSUPERVISED LEARNING WITH ADAPTIVE DATA GENERATION AND BIDIRECTIONAL MAPPINGS
Collecting a large dataset with high quality annotations is expensive and timeconsuming. Recently, Shrivastava et al. (2017) propose Simulated+Unsupervised (S+U) learning: It first learns a mapping from synthetic data to real data, translates a large amount of labeled synthetic data to the ones that resemble real data, and then trains a learning model on the translated data. Bousmalis et al. (2017b) propose a similar framework that jointly trains a translation mapping and a learning model. While these algorithms are shown to achieve the state-of-the-art performances on various tasks, it may have a room for improvement, as they do not fully leverage flexibility of data simulation process and consider only the forward (synthetic to real) mapping. Inspired by this limitation, we propose a new S+U learning algorithm, which fully leverage the flexibility of data simulators and bidirectional mappings between synthetic and real data. We show that our approach achieves the improved performance on the gaze estimation task, outperforming (Shrivastava et al., 2017).

Review:
Review, ICLR 2018, Simulated+Unsupervised Learning With Adaptive Data Generation and Bidirectional Mappings
Summary:
The paper presents several extensions to the method presented in SimGAN (Shirvastava et al. 2017). 
First, it adds a procedure to make the distribution of parameters of the simulation closer to the one in real world images. A predictor is trained on simulated images created with a manually initialized distribution. This predictor is used to estimate pseudo labels for the unlabeled real-world data. The distribution of the estimated pseudo labels is used produce a new set of simulated images. This process is iterated. 
Second, it adds the idea of cycle consistency (e.g., from CycleGAN) in order to counter mode collapse and label shift. 
Third, since cycle consistency does not fully solve the label shift problem, a feature consistency loss is added.
Finally, in contrast to ll related methods, the final system used for inference is not a predictor trained on a mix of real and “fake” images from the real-world target domain. Instead the predictor is trained purely on synthetic data and it is fed real world examples by using the back/real-to-sim-generator (trained in the conjunction with the forward mapping cycle) to map the real inputs to “fake” synthetic ones.
The paper is well written. The novelty is incremental in most parts, but the overall system can be seen as novel. 
In particular, I am not aware of any published work that uses of the (backwards) real-to-sim generator plus sim-only trained predictor for inference (although I personally know several people who had the same idea and have been working on it). I like this part because it perfectly makes sense not to let the generator hallucinate real-world effects on rather clean simulated data, but the other way around, remove all kinds of variations to produce a clean image from which the prediction should be easier.
The paper should include Bousmalis et al., “Unsupervised Pixel-Level Domain Adaptation With Generative Adversarial Networks”, CVPR 2017 in its discussion, since it is very closely related to Shirvastava et al. 2017.
With respect to the feature consistency loss the paper should also discuss related work defining losses over feature activations for very similar reasons, such as in image stylization (e.g. L. A. Gatys et al. “Image Style Transfer Using Convolutional Neural Networks” CVPR 2016, L. A. Gatys et al. “Controlling Perceptual Factors in Neural Style Transfer” CVPR 2017), or the recently presented “Photographic Image Synthesis with Cascaded Refinement Networks”, ICCV 2017.
Bousmalis et al., “Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping”, arXiv:1709.07857, even uses the same technique in the context of training GANs.
Adaptive Data Generation:
I do not fully see the point in matching the distribution of parameters of the real world samples with the simulated data. For the few, easily interpretable parameters in the given task it should be relatively easy to specify reasonable ranges. If the simulation in some edge cases produces samples that are beyond the range of what occurs in the real world, that is maybe not very efficient, but I would be surprised if it ultimately hurt the performance of the predictor. 
I do see the advantage when training the GAN though, since a good discriminator would learn to pick out those samples as generated. Again, though, I am not very sure whether that would hurt the performance of the overall system in practice.
Limiting the parameters to those values of the real world data also seems rather restricting. If the real world data does not cover certain ranges, not because those values are infeasible or infrequent, but just because it so happens that this range was not covered in the data acquisition, the simulation could be used to fill in those ranges. 
Additionally, the whole procedure of training on sim data and then pseudo-labeling the real data with it is based on the assumption that a predictor trained on simulated data only already works quite well on real data. It might be possible in the case of the task at hand, but for more challenging domain adaptation problem it might not be feasible. 
There is also no guarantee for the convergence of the cycle, which is also evident from the experiments (Table 1. After three iterations the angle error increases again. (The use of the Hellinger distance is unclear to me since it, as explained in the text, does not correspond with what is being optimized). In the experiments the cycle was stopped after two iterations. However, how would you know when to stop if you didn’t have ground truth labels for the real world data?
Comparisons:
The experiments should include a comparison to using the forward generator trained in this framework to train a predictor on “fake” real data and test it on real data (ie. a line “ours | RS | R | ?” in Table 2, and a more direct comparison to Shrivastava). This would be necessary to prove the benefit of using the back-generator + sim trained predictor.
Detailed comments:
* Figure 1 seems not to be referenced in the text.
* I don’t understand the choice for reduction of the sim parameters. Why was, e.g., the yaw and pitch parameters of the eyeball set equal to those of the camera? Also, I guess there is a typo in the last equality (pitch and yaw of the camera?).
* The Bibliography needs to be checked. Names of journals and conferences are inconsistent, long and short forms mixed, year several times, “Proceedings” multiple times, ...