In many machine learning applications, we are faced with incomplete datasets. In the literature, missing data imputation techniques have been mostly concerned with filling missing values. However, the existence of missing values is synonymous with uncertainties not only over the distribution of missing values but also over target class assignments that require careful consideration. In this paper, we propose a simple and effective method for imputing missing features and estimating the distribution of target assignments given incomplete data. In order to make imputations, we train a simple and effective generator network to generate imputations that a discriminator network is tasked to distinguish. Following this, a predictor network is trained using the imputed samples from the generator network to capture the classification uncertainties and make predictions accordingly. The proposed method is evaluated on CIFAR-10 image dataset as well as three real-world tabular classification datasets, under different missingness rates and structures. Our experimental results show the effectiveness of the proposed method in generating imputations as well as providing estimates for the class uncertainties in a classification task when faced with missing values.
This is a nice piece of incremental work on top of previously published GAN imputation methods. It seems to work well in the limited evaluation and is at least claimed to be easier to use for practitioners. This paper could benefit tremendously from both better evaluation and discussion. The paper would be much clearer if GI contextualized itself relative to GAIN on the one hand (which is the most similar GAN method) and multiple imputation on the other hand (of which this is almost, but not quite, an instance of). 
Suggestions for improving the introduction & discussion: 
* The purpose of this paper is to model uncertainties about missing values — you really should say more about probabilistic methods than "A few exceptions exist such as Bayesian models”.  At least give some motivation for why certain imputations problems couldn’t be feasibly solved by modeling the missing values in a probabilistic programming framework. 
* Other GAN methods for imputation (GAIN and MisGAN) are dismissed as "often very complicated to be applied in practical setups by practitioners”. Given that the described method resembles GAIN, is it really much simpler? If so, can you be more specific when characterizing related work?
* "This is different from approaches such as multiple imputation where several predictors are trained on different imputed versions of a dataset.” — the main difference between this approach and MI is that you’re interleaving imputation and training a downstream model. Emphasize this earlier on, since it will make the whole technique easier to understand.
Suggestions for improving the evaluation:
* You’re imputing missing rectangles from an image dataset — please show us the resulting images. I would greatly prefer this to Section 4.5 — which is a very low sample size, low dimensionality example and it’s really unclear how well it generalizes to real data. 
* "We also considered using root means squared error (RMSE); however, we decided not to use this measure as we observed an inconsistent behavior using RMSE in our comparisons as RMSE favors methods that show less variance rather than realistic and sharp samples from the distribution.” — I think this is a mistake, likely motivated by the proposed method doing worse under the RMSE metric. Show us several relevant metrics and then discuss their tradeoffs afterward. 
* "We run each experiment multiple times (at least 4)” — please report how often each experiment was run, even better if you standardize this number. 
* For Table 2, please provide accuracy without missing values as a baseline.
* Add MICE or some other “standard” imputation method as a baseline. 
Suggestions for improving readability: 
* Many sentences start with “in this” (e.g. “in this case”, “in this setting”, &c). Sometimes these sentences even co-occur within the same paragraph. Try to switch up the phrasing and move away from repetition. 
* Not a complete sentence: "For instance, jointly training multiple generator/discriminator networks, tuning objective functions with multiple hyper-parameters, etc."
Update: I think the latest draft of the paper is a big improvement, the inclusion of a "classical" baseline, improved language and additional appendices are all welcome. I'm leaving the rating as a "weak accept" since the paper still feels rough and could use additional editing/streamlining.