text: This work attempts to improve the global consistency of samples generated by generative adversarial networks by replacing the discriminator with an autoregressive model in an encoded feature space.  The log likelihood of the classification model is then replaced with the log likelihood of the feature space autoregressive model.  It's not clear what can be said with respect to the convergence properties of this class of models,  and this is not discussed. The method is quite similar in spirit to Denoising Feature Matching of Warde-Farley & Bengio (2017),  as both estimate a density model in feature space -- this method via a constrained autoregressive model and DFM via an estimator of the score function,  although DFM was used in conjunction with the standard criterion whereas this method replaces it.  This is certainly worth mentioning and discussing.  In particular the section in Warde-Farley & Bengio regarding the feature space transformation of the data density seems quite relevant in this work. Unfortunately the only quantitative measurements reporter are Inception scores,  which is known to be a poor measure  (and the scores presented are not particularly high, either);  Frechet Inception distance or log likelihood estimates via AIS on some dataset would be more convincing.  On the plus side, the authors report an average over Inception scores for multiple runs.  On the other hand, it sounds as though the stopping criterion was still qualitative.