Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper proposes a novel regularization scheme called Neuron Steadiness Regularization (NSR) which aims to reduce variance between activations belonging to a class. NSR is well motivated and well situated among related work in the paper. It is further verified empirically tested on wide variety of benchmark/model-combinations.
strengths_and_weaknesses: Strengths
- The paper does a good job of clearly explaining the proposed method and even discusses practical concerns like estimation error accrued by statistics being used.
- Research questions and experiments are thorough and well thought out, especially how NSR fares against other regularization methods seems quite pertinent.

Weaknesses
- My main set of suggestions are related to making the paper stronger by how far we can battle test NSR in practical settings, more specifically
  - The improvements with NSR can be magnified using stronger baselines, for instance, this paper (https://arxiv.org/abs/2010.01412) obtained much lower Top-5 error rate when training ResNet-50 on Imagenet for 200 epochs.
  - In Table 4 and 5, one way to merge these would be to add additional results for maybe at least ResNet-18 where both dropout and BN are typically used. I would also try to combine positive regularization methods for all 3 models with NSR and see if they provide orthogonal improvements to NSR.
questions: In addition to what I mentioned in Strengths And Weaknesses section, I have following suggestions/questions:

- I wonder, despite adding NSR to multiple layers have overlapping benefits, what happens if we employ it in *all* the layers? This will likely simplify usage of this method since now it's not required to explore which layer activations to use. 
- Another suggestion I had was to study qualitative effects of training with NSR, for example, does NSR implicitly penalizes sharpness? If yes, since SAM (https://arxiv.org/abs/2010.01412) has a larger computational overhead, can NSR be used in lieu of it with similar benefits?
- l311 typo: "Actiavtion"
limitations: Yes
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper studies the intra-class neuron response variance in neural networks. The authors observe that this variance is lower for correctly classified samples, and hence propose to use this as a regularization term. They then show the effectiveness of this regularization compared to the vanilla case and other regularizations such as the Jacobian norm. 
strengths_and_weaknesses: Strengths:

1- There are enough experiments presented to show the improvement compared to the vanilla case. It would be even better to have the same amount of experiments to compare with other regularization techniques as well (they are only presented in Table 4).

2- The metric is novel and interesting to the community. 

Weaknesses:

1- The paper structure needs improvements. Section 2 is repetitions of what was already presented in Section 1. Also, the paper needs proofreading to fix typos.

2- Most of the ablation studies (comparing deeper and shallower networks and layer selection for computation) are done on MLPs. Performing these studies on CNNs is more interesting to practitioners.

3- Comparison of the neuron response variance to similar metrics such as input-output sensitivity is missing.
questions: 1- It is claimed that neuron intra-class response variance has a high correlation with classification accuracy. However, this has not been directly shown directly. It is only shown that the variance is lower for correctly classified samples. How would a plot look up with the y-axis as accuracy and the x-axis as the variance for various experimental settings? This should be then compared with similar metrics in terms of correlation such as output sensitivity to input perturbations which are studied in papers like [1]. Then it would be interesting to see the particular information this metric brings as opposed to the previous ones.

2- Why not consider the zero response in the calculations? Even an inactivated state contains information by itself.

3- Have you studied the effect of using different regularization intensity values (lambda_n) for each sample? What is the effect of that?
If this has not been studied, then I would suggest not introducing it at all and only presenting a single lambda.

4- For MLPs deeper models observe a higher performance gain. However, this is not observed for CNNs (in Table 2). What could be the explanation for this?


[1] Sensitivity and Generalization in Neural Networks: An Empirical Study, Novak et al., ICLR 2018.
limitations: The mention "We also systematically consider the border impact, No risk is found."
They don't mention the limitations of the method and where it would possibly break.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper suggests that neurons with similar responses to instances of the same class may lead to better generalization. Accordingly, the authors propose a regularization method to reduce neuron intra-class response variance. This method outperforms the vanilla version of the optimization algorithm under the given settings on MLP, CNN, and GNN models. And the computational cost seems acceptable.
strengths_and_weaknesses: Pros:
- The proposed regularization method is consistent with the idea of the Consistency of Representations Complexity and is well-motivated in this sense.
- The authors propose several practical ways to reduce computational budget and storage cost, e.g., saving a sequence of summation values of neurons' responses or regularizing only one particular layer. The experimental results show the effectiveness of these adjustments.
- This paper is well organized and clearly written. Most claims are well supported by theoretical analysis or experimental results.

Cons:
- The experiments are somehow limited. The generalization performance of models when using NSR under input corruptions is unclear. Given that this method requires the label information, evaluating the robustness to label noise is kind of necessary to verify its practical values.
- In order to utilize this regularization efficiently, one needs to choose the layer with the largest aggregated neuron intra-class response variance, which seems not to be directly accessible and requires additional calculations.
questions: - When labels of training data get corrupted, how will the performance of NSR be affected? Will the Consistency of Representations Complexity become less informative when the given labels are not the ground truth labels?
- When using selecting the layer to apply NSR, does the model need to be trained for a few epochs, and why?

limitations: Utilizing NSR method requires well-annotated data, which limits it to only supervised learning problems (though the reviewer don't consider this as a major flaw and there is no need to work on solving it here).
review_title: null

Meta-review: metareview: This paper measures intra-class neuron response variance, and shows that network performance is better when it is lower. They then use this term as a regularization target, and show that it leads to improved model performance.

Reviews were high quality. Scores were between weak accept and accept, with one reviewer raising their score from weak accept to accept. The most significant concerns were experimental: around ablations, around the diversity and scale of models the technique was tested on, and around the tuning of baselines. However, the experiments seemed fairly strong as is, and of course there are always more experimental conditions that can be requested.

Based upon the reviewer consensus, I also recommend acceptance for this paper.
confidence: Less certain
Decision: Accept