GANs provide a framework for training generative models which mimic a data distribution. However, in many cases we wish to train a generative model to optimize some auxiliary objective function within the data it generates, such as making more aesthetically pleasing images. In some cases, these objective functions are difficult to evaluate, e.g. they may require human interaction. Here, we develop a system for efficiently training a GAN to increase a generic rate of positive user interactions, for example aesthetic ratings. To do this, we build a model of human behavior in the targeted domain from a relatively small set of interactions, and then use this behavioral model as an auxiliary loss function to improve the generative model. As a proof of concept, we demonstrate that this system is successful at improving positive interaction rates simulated from a variety of objectives, and characterize some factors that affect its performance.
This paper proposes a technique to improve the output of GANs by maximising a separate score that aims to mimic human interactions. 
Summary:
The goal of the technique to involve human interaction in generative processes is interesting. The proposed addition of a new loss function for this purpose is an obvious choice, not particularly involved. It is unclear to me whether the paper has value in its current form, that is without experimental results for the task it achieves. It feels to premature for publication. 
More comments:
The main problem with this paper is that the proposed systems is designed for a human interaction setting but no such experiment is done or presented. The title is misleading, this may be the direction where the authors of the submission want to go, but the title  “.. with human interactions” is clearly misleading. “Model of human interactions” may be more appropriate. 
The technical idea of this paper is to introduce a separate score in the GAN training process. This modifies the generator objective.  Besides “fooling” the discriminator, the generator objective is to maximise user interaction with the generated batch of images. This is an interesting objective but since no interactive experiments presented in this paper, the rest of the experiments hinges on the definition of “PIR” (positive interaction rate)using a model of human interaction. Instead of real interactions, the submission proposes to maximise the activations of hidden units in a separate neural network. By choosing the hierarchy level and type of filter the results of the GAN differ. 
I could not appreciate the results in Figure 2 since I was missing the definition of PIR, how it is drawn in the training setup. Further I found it not surprising that the PIR changes when a highly parameterised model is trained for this task. The PIR value comes from a separate network not directly accessible during training time, nonetheless I would have been surprised to not see an increase. Please comment in the rebuttal and I would appreciate if the details of the synthetic PIR values on the training set could be explained.
- Technically it was a bit unclear to me how the objective is defined. There is a PIR per level and filter (as defined in C4) but in the setup the L_{PIR} was mentioned to be a scalar function, how are the values then summarized? There is a PIR per level and feature defined in C4. 
- What does the PIR with the model in Section 3 stand for? Shouldn’t be something like “uniqueness”, that is how unique is an image in a batch of images be a better indicator? Besides, the intent of what possibly interesting PIR examples will be was unclear. 
E.g., the statement at the end of 2.1 is unclear at that point in the document. How is the PIR drawn exactly? What does it represent? Is there a PIR per image? It becomes clear later, but I suggest to revisit this description in a new version.
- Also I suggest to move more details from Section C4 into the main text in Section 3. The high level description in Section 3.