Paper:
Generalizing GANs: A Turing Perspective
Recently, a new class of machine learning algorithms has emerged, where models and discriminators are generated in a competitive setting. The most prominent example is Generative Adversarial Networks (GANs). In this paper we examine how these algorithms relate to the Turing test, and derive what-from a Turing perspective-can be considered their defining features. Based on these features, we outline directions for generalizing GANs-resulting in the family of algorithms referred to as Turing Learning. One such direction is to allow the discriminators to interact with the processes from which the data samples are obtained, making them "interrogators", as in the Turing test. We validate this idea using two case studies. In the first case study, a computer infers the behavior of an agent while controlling its environment. In the second case study, a robot infers its own sensor configuration while controlling its movements. The results confirm that by allowing discriminators to interrogate, the accuracy of models is improved.

Review:
This paper proposes a generalization of GAN, which the authors refer to as Turing Learning. The idea is to let the discriminator to interact with the generator, and thus work as an active interrogator that can influence the sampling behavior of the generator. To show that this strategy is superior to existing GAN which has the discriminator to function passive responder, the authors perform two case studies of the proposed (high-level) model (interactive), comparing against models with passive discriminators. The results show that the interactive model largely outperforms the passive models, with much less variances across multiple runs.
Pros
1) The paper is written well, with clear motivation. The idea of having the discriminator to work as active interrogator to influence the generator is novel and makes perfect sense. 
2) The results show that the proposed addition of discriminator-generator interaction actually helps the model work more accurately. 
Cons
3) The proposed learning strategy is too general while the implemented models are too specific to each problem. I was hoping to see a specific GAN learning framework that implements the idea, but no concrete model is proposed and the interaction between the discriminator and the generator is implemented differently from model to model, in the two case studies. This severely limits the application of the model to new problems. 
4) The two case studies consider toy problems, and it is not clear how this model will work in real-world problems. This gives me the impression that the work is still preliminary. 
Summary
In sum, the paper presents a novel and  interesting idea that can generalize and potentially improve the existing generative adversarial learning. However, the idea is not implemented into a concrete model that can generalize to general learning cases, and is only validated with two proof-of-concept case studies on toy problems. This limits the applicability of the model to new problems. Thus, despite its novelty and the potential impact, I think this is a preliminary work at its current status, and do not strongly support for its acceptance.