Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 5: Marginally below the acceptance threshold
review_content: summary: The model aims to train a pair of two ConvNets in the setup of the so-called discrimination game -- one ConvNet (sender) can pass discrete information to the other ConvNet (receiver) to identify the same image presented to the Sender among a set of images. Generating such discrete (bottlenecked) information is learned in an end-to-end manner, which is claimed to provide a human-interpretable inter-ConvNet communication. Compared to SimCLR (Chen et al., ICML 2020), the proposed method shows better or matched performance in terms of discrimination game-playing accuracy and downstream image classification task.

main_review: The paper needs to be more organized and focused. As the main task that the paper aims to solve is the communication between two deep neural networks, I would expect to see a more thorough analysis of the communication. E.g. (i) why |V| is chosen as 2048 and what would happen with larger or smaller values, (ii) sparsity or entropy of V, and/or (iii) what information V is learning. 

Moreover, the proposed model is mainly compared with SimCLR, a self-supervised image representation learning method, but such a comparison does not look fair. SimCLR is mainly trained to make closer the latent representations of the same image of different views, thus simply argmax-ing SimCLR’s internal representations for computing game-playing accuracy (Table 1) seems unfair.

Plus, as summarized in Section 2.1, it looks like there is some prior work for the deep net emergent communication task, but surprisingly none of them is compared. Some prior work has already proposed to use the discrimination game and to use discrete communication protocol. As I am not closely following this field, it is very difficult to judge its novel contributions and their significance. For me, the contribution looks like training the existing model in an end-to-end manner. I would expect to see the author’s response on this.

limitations_and_societal_impact: The paper does not address the limitations and societal impact of their work. I would suggest adding a discussion section to describe the potential limitations of the proposed model.
review_title: null

final_score: rating: 8: Top 50% of accepted NeurIPS papers, clear accept
review_content: summary: This paper investigates a large-scale emergent communication task, where a speaker and listener agent learn to communicate in a referential game, and draws connections between emergent communication setups and recent advances in self-supervised learning (e.g. SimCLR). 

The authors show that it is possible to train emergent communication agents to such a degree that the learned communication protocol is interpretable, generalizes to unseen object categories, and most surprisingly, learns visual representations that are comparable to SimCLR.

One of the key insights enabling such a result is borrowed from the contrastive learning literature: the authors propose using standard data augmentation techniques to make the speaker and listener see different "views" of the same image, to prevent overfitting to low-level visual features present in the input. This is a common concern of emergent communication systems. They convincingly show via Gaussian blob experiments that such augmentation applied at scale prevents degenerate communication schemes, and only with such an augmentation are such high-quality visual representations learnable.

The setup is a fairly typical one used in the emergent communication literature: speaker and listener are parameterized by (possibly seperate) convolutional networks, and the speaker learns to embed the identity of a target image into a one-hot vector, which is given to a listener which must identify the referred target. The entire system is end-to-end differentiable.

Overall, this paper, with its striking finding that the visual representations learned by emergent protocols are as good as SimCLR, makes an exciting step towards more seriously considering  emergent communication-based setups as an alternative way to learn good visual (and linguistic) representations for a variety of downstream tasks.
main_review: ## Strengths

- Overall, this paper is very good: it presents a clear and highly scalable emergent communication setup, at a larger scale that has been attempted before, and shows that agents are able to communicate with an interpretable protocol that generalizes to unseen categories. There are a lot of experimental details given in the paper which should facilitate reproducibility of the presented results, and the released codebase will hopefully make this easy.
- The most striking finding is that the visual features learned by agents in this game are able to perform on par with SimCLR (though this is a reimplementation, which doesn't achieve quite as high performance as the best model in the original paper).
- Theoretically, I appreciate the new connections between recent self-supervised learning techniques (SimCLR) and emergent communication-based setups. I especially like the authors idea of casting SimCLR as a two-player game. Drawing these connections allow us to bring many of the insights from self-supervised learning to bear on the emergent communication problems.
- Along these lines, one of the notable borrowed insights is the idea of using data augmentation to define different "views" of an image given to speaker and listener. This greatly increases robustness of the learned communication and visual representations, which is a very common concern in the emergent communication literature (Bouchacort and Baroni 2018, Kottur et al. 2017), and (as noted in Footnote 1) has typically been ameliorated with handcrafted, non-scalable solutions. The Gaussian blobs experiments are very astute inclusions here.
- Even the naive casting of SimCLR as a zero-shot "emergent communication" baseline is interesting (e.g. that the argmax is meaningful).

## Weaknesses

- The paper does not have much significant technical novelty; it seems to be a fairly straightforward extension of a now-standard emergent communication setup in the literature. I don't see this as a crucial weakness, however. The increased scale (and the interesting tricks that enable scalable training) are drawn from the papers biggest contribution, which is the connections to the self-supervised learning literature.
- It's unclear how truly OOD the collected OOD images are, and how difficult generalization to a few held-out ImageNet categories is in this setup. At least from Figure 4, the OOD images do not seem *that* OOD to me (we still have birds for Symbol 1, for example). A figure depicting sampled OOD images and their closest in-domain WordNet classes might be useful here. More interesting would be to compare generalization outside ImageNet (e.g. any of the adversarial ImageNet datasets, or other datasets entirely, e.g. the ones used for transfer in Table 3).
- The paper could compare and contrast emergent communication-based setups to other unsupervised learning methods. For example, especially in the shared CNN setting, an emergent communication task looks very similar to a discrete autoencoder (which can be used as an "unsupervised image annotation" tool, whose features can be reused for supervised learning, etc). What are the benefits of casting this as a referential game - presumably that it is much easier/more tractable to learn to distinguish between images, rather than reconstruct ones (which is a similar purported advantage of contrastive learning)?
- It may be better to report adjusted mutual information (Vinh et al., JMLR 2010; [https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_mutual_info_score.html#sklearn.metrics.adjusted_mutual_info_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_mutual_info_score.html#sklearn.metrics.adjusted_mutual_info_score)) rather than normalized mutual information in Table 2, as the former is adjusted for chance.

## Questions

- One of the interesting findings of the Bouchacort and Baroni paper is that, even in the referential game with different images presented to both speaker and listener, the agents are still able to generalize to gaussian blobs. What is different, then, about the augmentation+ strategy presented in this paper that makes it unable to generalize to gaussian blobs? Is it simply increased scale?
- Have authors explored the effect of vocabulary size?
- What about the number of distractor images in each game?
limitations_and_societal_impact: **No**: In L457 of the checklist, I would caution against authors claiming that a discrete channel for multi-agent communication is *inherently* "interpretable" and will unequivocally "have a positive impact in terms of explainable AI." In fact there are many cases in the literature (e.g. Kottur et al., 2017) where such discrete protocols are distinctly uninterpretable, and care must be taken not to naively assume otherwise. Obviously encouraging more interpretable protocols, e.g. via the data augmentation methods described here, is a good step, but does not guarantee interpretability and positive impact. I'd encourage the authors to consider this more carefully either in the main text or in the supplement.
review_title: null

final_score: rating: 5: Marginally below the acceptance threshold
review_content: summary: This paper studies the visual features emerging from a discrete referential game between networks. A sender network processes the input image into a discrete symbol; the receiver is tasked to identify the input image from discrators images using the encoded discrete symbol. The authors study the emerging features based on their referential accuracy, interpretability, and quality as unsupervised pre-trained features.

main_review: === Strengths ===

+ The paper is well written and the presentation is easy to follow

+ I appreciate the analysis on interpretability in section 4. The WNsim score computed using WordNet taxonomy shows that the emergent features are semantically meaningful. The analysis on the sparsity of SimCLR features, demonstrated by its interpretability scores, is particularly useful.

+ The downstream object classification results in Table 3 are competitive against the strong baseline SimCLR. This shows great potential for the proposed referential as an alternative self-supervised pre-training method.

=== Weaknesses ===

- One of the main contributions the authors claim is that the visual features emergent from the referential game is of high quality. The authors argue this by showing competitive numbers against SimCLR in downstream classification, both with frozen features. I would like to also see the comparisons on fine-tuned features, which measure the usefulness of the emergent features more directly.

- I am confused on why there is no data augmentation during testing in the referential game (L181-182). This means the exact same image is present in the candidate images. Wouldn’t this mean that any deterministic network acting as both the sender and receiver, even with completely randomized weights, can achieve 100% accuracy, assuming there are no ties?

- The authors claim that this paper provides evidence that communication protocols emerge in a realistic setting (L8-10). In my opinion (my opinion only), this is slightly sensationalist.
Firstly, I disagree that the emergence feature can be considered as “communication protocols”, as they merely describe the implicit class of the input image. Admittedly, they are emergent and discrete. However, prior work has already shown that it is possible to learn compact discrete latent variables of images, without any supervision [1,2]. Secondly, I disagree that the described referential game is a realistic setting, as the receiver agent is only tasked to identify **the exact same image** that the sender encodes. 


[1] Neural Discrete Representation Learning, van den Oord et al., 2017

[2] Generating Diverse High-Fidelity Images with VQ-VAE-2, Razavi et al., 2019

limitations_and_societal_impact: Discussed in the paper.
review_title: null

Meta-review: Although ratings were quite divergent, I find myself in agreement with the reviewer arguing for acceptance. There are several interesting empirical findings in this work, and I find the connections being established between emergent communication and contrastive learning very interesting. I would have liked to see referential games with message lengths greater than one and experiments varying the way distractors were sampled. Though, I do appreciate these being called out explicitly as future work. Please incorporate the additional results and clarifications from your rebuttal/discussions into the camera ready version of the paper. A couple of small suggestions on my side: I think it could be smart to reduce the size of Figure 3 and use the extra space to walk the reader through the abstract connection you are making more thoroughly. The core of the argument and the setup for why comparing with SimCLR makes sense appear to be things that readers could misunderstand, and perhaps the authors can guard against this with a bit more discussion of them. Finally, thank you for your engagement with the reviewers during the rebuttal period.
Decision: Accept (Poster)