Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 3: Weak Reject
review_content: The paper introduces a fairly simple yet seemly effective method for quantizing GAN. Existing quantization methods (namely minmax, log, and tanh quantization) for CNN/RNN fail brutally under GAN setting. From empirical observation of the distribution of quantized weights, the authors conjecture the reason being under-utilization of the low-bit representation, called under-representation in the paper. Based on such observation, linear scaling with EM is proposed and experimental results seem to be effective. 

[Advantage]
The paper is clearly written and easy to follow. The proposed method is well-motivated from the empirical observation presented in Sec 3, and seems to mitigate the difficulties from the discussion in Sec 5.

[Disadvantage & Improvement]
While I am not a direct expert in this area, I do have some concerns regarding the novelty of the method and comparison to previous works. Linear quantization seems to be a common/intuitive method and there are various improvement techniques built upon it (e.g. cliping, ocs,... etc [1,2]). These are related works, yet neither included nor discussed in this paper. How is the presented linear+EM method comparing with these variants, in term of effectiveness on training GAN and the reported test-time metrics? In short, the comparison to previous works seems insufficient in my point of view.

Also, can you comment on Defensive Quantization (DQ) [3]? The quantization method is specifically designed for adversarial attack/perturbation setting and seems applicable under GAN setting. 

Last, there is a typo at the end of Sec 4.2: should it be f_{em}(x) instead of f_{e}m(x)?

[1] Low-bit Quantization of Neural Networks for Efficient Inference
[2] Improving Neural Network Quantization using Outlier Channel Splitting
[3] Defensive Quantization: When Efficiency Meets Robustness


review_title: Official Blind Review #1

final_score: rating: 6: Weak Accept
review_content: This paper propose to study the quantization of GANs parameters. They show that standard methods to quantize the weights of neural networks fails when doing extreme quantization (1 or 2-bit quantization). They show that when using low-bit representation, some of the bits are used to model extremal values of the weights which are irrelevant and lead to numerical instability. To fix this issue they propose a new method based on Expectation-Maximization to quantify the weights of the neural networks. They then show experimentally that this enables them to quantize the weights of neural networks to low bit representation without a complete drop of performance and remaining stable.

I'm overall in favour of accepting this work. The paper is well motivated, the authors clearly show the benefits of the proposed approach compared to other approach when using extreme quantization.

Main argument:
+ Great overview of previous methods and why they fail when applying extreme quantization
+ Great study of the influence of the sensitivity to the number of bits used for quantization
- It would have been nice if the author had provided standard deviation for the results by running each method several times. In particular figure 2.c seem to show that they might be a lot of variance in the results when using low bit quantization.
- I feel some details are missing or at least lack some precision. For example are the networks pre-trained with full precision in all experiments ? if so can you precise it in section 3.1 also ? 
- The proposed approach seem very similar in spirit to vector quantization, can the author contrast their method to vector quantization ?
- In equation (7) doesn't the constant C also depend on alpha and beta ?
- In section 5.1 do you also use the two phase training described in section 4.2 ?
- Figure 4.c seems to indicate that quantize the generator only is no more a problem ? Can you explain why this figure is very different from figure 2.c 
- In table 3 how is the number of bits chosen, did you try several different values and report the best performance ?

Minor:
- Some of the notations are a bit confusing. You call X the tensor of x, I think it would be more clear to say that X is the domain of x.
- I'm surprised by the results in section 3.1, wouldn't the issue described in this section when training standard neural networks ? wasn't this known before ?
- There is some typos in the text
review_title: Official Blind Review #2

final_score: rating: 3: Weak Reject
review_content: Summary:
The authors address the quantization of Generative Adversarial Networks (GANs). The paper first performs a sensitivity study for both the generator and the discriminator to quantization methods. Building upon the conclusion of this study, the authors propose a scalar quantization method (QGAN) and compress models to 1 bit of 2 bits weights and show generated images and metrics by the compressed models. 

Strengths of the paper:
- As well stated in the introduction, the compression of GANs (in particular the generator, which is used at inference time) is of practical interest and, to the best of my knowledge, novel. This novelty can be explained by (1) the fact that it takes tome for quantization methods to percolate the entire deep learning field and/or (2) the fact that quantizing GANs has its specificities and own challenges that have not been yet addressed (this is the claim of the authors).
- The sensitivity study is of interest for the community that can build upon this work. The conclusions (discriminator more sensitive than generator to quantization, quantizing both generator and discriminator helps) are sensible and interesting. 

Weaknesses of the paper:
- The related work section could be greatly improved, thereby showing the limited novelty of the proposed method (QGAN). Indeed, the authors propose to learn the optimal scaling parameters alpha and beta. Many works perform this already and are currently missing in this section, see for instance the two recent surveys: "A Survey on Methods and Theories of Quantized Neural Networks", Guo, "A Survey of Model Compression and Acceleration for Deep Neural Networks", Cheng et al. 
- Results. The results are not sufficient to justify the performance of the method for two reasons. (1) First, the scale is crucial in assessing the performance of a quantization method. As an example, it is easier to quantize small ResNets on CIFAR-10 than large ResNets on ImageNet. Thus, scales enables to better discriminate between various approaches. I acknowledge that this requires large computational resources but this would greatly strengthen the paper (2) Second, GAN metrics have known shortcomings (see for instance "How good is my GAN?", Shmelkov et al.), so the strength of Table 2 is limited. This is in part alleviated by the authors by showing generated images (which is a good practice), but again echoing point (1), larger images would have helped assess better the quality of the quantization.

Justification of rating: 
The authors propose a sensitivity study that is interesting for the community. However, the proposed method lacks novelty and the results are not convincing enough. I encourage the authors to pursue in this interesting direction which has important practical implications.
review_title: Official Blind Review #3

Meta-review: main summary:  method for quantizing GAN

discussion:
reviewer 1: well-written paper, but reviewer questions novelty
reviewer 2: well-written, but some details are missing in the paper as well as comparisons to related work
reviewer 3: well-written and interesting topic, related work section and clarity of results could be improved
recommendation: all reviewers agree paper could be improved by better comparison to related work and better clarity of presentation. Marking paper as reject.
Decision: Reject