Paper:
Generative Adversarial Networks (GANs) learn a generative model by playing an adversarial game between a generator and an auxiliary discriminator, which classifies data samples vs. generated ones. However, it does not explicitly model feature co-occurrences in samples. In this paper, we propose a novel Autoregressive Generative Adversarial Network (ARGAN), that models the latent distribution of data using an autoregressive model, rather than relying on binary classification of samples into data/generated categories. In this way, feature co-occurrences in samples can be more efficiently captured. Our model was evaluated on two widely used datasets: CIFAR-10 and STL-10. Its performance is competitive with respect to other GAN models both quantitatively and qualitatively.

Review:
This paper proposes an alternative GAN formulation that replaces the standard binary classification task in the discriminator with a autoregressive model that attempts to capture discriminative feature dependencies on the true data samples.
Summary assessment:
The paper presents a novel perspective on GANs and an interesting conjecture regarding the failure of GANs to capture global consistency. However the experiments do not directly support this conjecture. In addition, both qualitative and quantitative results to not provide significant evidence of the value of this technique over and above the establish methods in the literature.
The central motivation of the method proposed in the paper, is a conjecture that the lack of global consistency in GAN-generated samples is due to the binary classification formulation of the discriminator. While this is an interesting conjecture, I am somewhat unconvinced that this is indeed the cause of the problem. First, I would argue that other high-performing auto-regressive models such as PixelRNN and PixelCNN also seem to lack global consistency. This observation would seem to violate this conjecture. More importantly, the paper does not show any direct empirical evidence in support of this conjecture. 
The authors make a very interesting observation in their description of the proposed approach. In discussing an initial variant of the model (Eqns. (5) and (6) and text immediately below), the authors state that attempting to maximize the negative log likelihood of the auto-regressive modelling the generated samples results in unstable training. I would like to see more discussion of this point as it could have some bearing on the difficulty of GAN to model sequential data in general. Does the failure occurs because the auto-regressive discriminator is able to "overfit" the generated samples?
As a result of the observed failure of the formulation given in Eqns. (5) and (6), the authors propose an alternative formulation that explicitly removes the negative likelihood maximization for generated samples. As a result the only objective for the auto-regressive model is an attempt to maximize the log-likelihood of the true data. The authors suggest that this should be sufficient to provide a reliable training signal for the generator. It would be useful if the authors showed a representation of these features (perhaps via T-SNE) for both true data and generated samples. 
Empirical results:
The authors experiments show samples (qualitative comparison) and inception scores (quantitative comparison) for 3 variants of the proposed model and compare these to methods in the literature. The comparisons show the proposed model preforms well, but does not exceed the performance of many of the existing methods in the literature.
Also, I fail to observe significantly more global consistency for these samples compared to samples of other SOTA GAN models in the literature. Again, there is no attempt made by the authors to make this direct comparison of global consistency either qualitatively or quantitatively. 
Minor comment:
I did not see where PARGAN was defined. Was this the combination of the Auto-regressive GAN with Patch-GAN?