Venue: NeurIPS 2022 Conference

Reviews:

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 finds an interesting problem with the residual networks, i.e., the network loafing problem. They find that the sub-networks will perform worse than those that are individually trained. Inspired by social psychology, they propose a stimulative training strategy, which randomly samples a residual sub-network and calculates the KL-divergence loss between the sampled sub-network and the residual network, to provide additional supervision to the sub-network. The proposed strategy is validated by a comprehensive theoretical and empirical analysis.
strengths_and_weaknesses: Originality: I like the idea to view the residual network from a social psychological perspective. This perspective is creative. Following the "unraveled view", they see the residual network as an ensemble of sub-networks and study the performance of each sub-network. They discover a novel problem called "network loafing", which has analogous to the problem in social psychology where an individual contributes less when in a group.
Quality: 
In general, the paper is technically sound. 
Clarity: Overall, the paper is well written and easy to understand. The motivation and the contributions are clear, and the whole paper is organized well.
Some minor issues with the writing: Some experimental details are missing, e.g., how do you measure the performance of subnetworks? Will you retrain the last logits layer? 
questions: 1. Can you explain why different sampling schemes make such a big difference (~5% in accuracy, figure 3)? It seems that in order for the main network to perform well, the subnetworks need to contain residual links inside. It will be great to see the explanation to this or see a comparison with normal training vs random sampling.
2. How do you measure the performance of subnetworks? Will you retrain the last logits layer? If you do not retain the last logits layer, then the performance drop is understandable because the range/distribution of the input data to the last logits layer has changed. Even if the last logits layer is not retrained, I would expect some scaling/correction when the residual connection is removed (removing the residual connection will somewhat downscale the signal). I think the performance of a subnetwork should be evaluated in a fair way and we need to include the necessary corrections. Because what we would like to know is how well the subnetwork itself is to perform classification and we should minimize the impact from other factors (e.g., the scaling effects).
3. I am curious about how does the proposed stimulative training strategy compared with providing supervision(the class label) directly to each layer? I am wondering if the authors directly compute CE loss between the labels and intermediate output instead of the KL loss, how the results would be like? I would like to know if the supervision to intermediate layers are the key to improve the performance and how effective the KL loss is.

limitations: See the questions above.
review_title: null

final_score: rating: 4: Borderline reject: Technically solid paper where reasons to reject, e.g., limited evaluation, outweigh reasons to accept, e.g., good evaluation. Please use sparingly.
review_content: summary: This paper explores the use of a training method for residual networks called "stimulative training". This method is inspired by the psychological phenomenon of "social loafing" where members of a group give less effort when in a group compared to when not in a group. The paper hypothesizes a similar "network loafing" phenomenon for neural networks where subnetworks in a larger network would perform suboptimally compared to when a similar network is trained by itself. The paper devises a training strategy called stimulative training that samples random subnetworks and minimizes a kl divergence loss between the output of the sampled subnetwork and the full network. The paper performs a few experiments to show that 
- The loafing problem exists with traditional training strategies and is eliminated/reduced with their method
- Their method improves the robustness of the network to deletion/permutation of layers
- Improves the overall performance of the network for CIFAR 100 and Imagenet datasets.
strengths_and_weaknesses: Strengths:
- Connecting the training of neural networks to the social loafing phenomenon was an interesting point. Showing that simply getting each individual subnetwork more supervision to a common goal leads to an improvement in performance is also an interesting result, as I could imagine that it could lead to worse generalization on test set (something like the group think phenomenon).
- I think the types of experiments selected was good. They covered a range of questions about this phenomenon.

Weaknesses:
- As far as I can tell, the results are based on only a single run of data. To draw the conclusions that the paper wants us to, I think they need to show that the results are replicable. For example, even if we say network loafing exists, it's unclear if we can conclude that it is a problem with a single run of data. Table 1 shows the results comparing the final accuracy of conventional training with stimulative training, and the improvement is small. To conclude that it is significant, the paper needs to show that it is replicable.
- The method seems fairly similar to self distillation. In self distillation, they have some added machinery to be able to use the method on architectures where the output of each layer might not be the same size. In this paper, because the architectures are limited to residual networks, that machinery is not needed. Still, it seems to be a special case of self distillation for residual networks (although the paper does examine the specific phenomenon of network loafing). It would be interesting to see something like self-distillation as a baseline.
- For Figure 4, please use more distinctive markers. It's difficult to tell the difference between markers of the same color.
questions: - When you mention sampling x subnetworks (eg sampling 144 subnetworks), is that on each step? If so, what is the difference in training time between the two methods?
- In section 3.3, when discussing the ordered residual sampling, the justification given for why ordered residual sampling is better than raw sampling is that because of the size of the space, raw sampling isn't able to train all the subnetworks enough. Does that mean that if you trained the method with raw sampling for longer, performance would continue improving until it beat the ordered residual sampling? Is the learning curve for raw sampling converged, or is it still improving?
- Is the sampling method uniform sampling?
- Did you try experiments with MSE instead of KL divergence for the stimulative training? Was there a difference?
limitations: Other than showing reproducibility, I'd mostly want to see a comparison to/discussion of a method like self-distillation, which is not limited by the type of architecture, which this method is. Assuming the network loafing phenomenon exists with other architectures as well, why shouldn't we use something like self-distillation instead of this method, since it's more general? Is there a big improvement in the ease of use or performance with resnets when using this method? Also, I am curious as to how much longer this method takes compared to conventional training.
review_title: null

final_score: rating: 3: Reject: For instance, a paper with technical flaws, weak evaluation, inadequate reproducibility and incompletely addressed ethical considerations.
review_content: summary: The paper proposes a view on residual network training from a social psychology perspective. An analogy is made where individual sub-networks of a ResNet are compared to people that are assigned a group project. Social loafing is the phenomenon that work is often not equally distributed in such groups. The paper argues that sub-networks do not perform equally on the classification task in a similar fashion and proposes two mechanisms (random sampling of blocks, and KL divergence between intermediate representations) as a remedy.
strengths_and_weaknesses: Strengths

The results show that the proposed mechanisms do improve the performance of the overall network and also individual sub-networks.

The writing of the paper is clear and the goal, method and results are presented in an easily understandable format. 

The paper contains many results of training networks with error bars which has high computational load.

Weaknesses

Framing: The grounding of the work in social psychology is extremely far fetched. ResBlocks are compared to people in a complex social group setting. The paper itself compares training a network to polynomial regression which has nothing to do with the complex social dynamics in a work group. There are no insights gained from this analogy and comparing a couple of ResBlocks to people contributes to false AI hype and other ethical issues. I strongly advocate for removing this framing from the paper.

Learning redundant blocks: A large portion of the analysis is aimed at the performance of individual sub networks, permuting and deleting residual blocks and the KL divergence between intermediate representations. In all these experiments, the proposed training strategy shows better results than conventional training. This is not very surprising as the additional objective of adding a KL divergence term between representations and sampling sub-networks trains the model exactly for these tasks. Moreover, both objectives essentially train the individual block to be redundant. (This is another breakdown point for the loafing analogy where the optimal group work outcome is not achieved when every member does the same job) It is questionable if this is a desired behavior as CNNs are commonly understood to learn a hierarchical structure of representations. If blocks can be reshuffled or deleted without affecting the performance much, this structure is lost. It is possible that this effect is less important on small images such as CIFAR100. 

Comparisons: The method is only compared to conventional training. Similar approaches have been explored that add auxiliary losses, distill representation (student teacher, KD, [12-19]) or drop residual blocks([22]) and it would be important to understand their effect on the network in comparison to the proposed approach. This would also make the comparison of dropping and reshuffling blocks more interesting.

questions: The questions mainly follow the weaknesses above.
1. Is the grounding of the work in social psychology fundamentally important to the presented approach? 

2. How can the objective of learning redundant residual blocks be combined with the understanding that CNNs extract feature hierarchies?

3. How does the method differ in performance to previous methods with similar ideas?


limitations: Limitations and societal impact are not discussed in the paper. Especially because of the framing as inspired by social psychology, it would be very important to discuss both aspects in the paper.
review_title: null

final_score: rating: 4: Borderline reject: Technically solid paper where reasons to reject, e.g., limited evaluation, outweigh reasons to accept, e.g., good evaluation. Please use sparingly.
review_content: summary: The authors argue that subnetworks in ResNet tend to perform sub-optimally due to a "social loafing effect". That is, since no single subnetwork is accountable for the final performance, they all tend to rely on the aggregate group performance rather than achieving competitive individual performance. In order to alleviate this problem, the authors propose including an additional loss term that encourages subnetworks to match the predictions of the group.  In experiments the authors show that not only their loss solves the loafing problem but also improves the overall final performance. An additional theoretical analysis shows that improving subnetworks is tied to improving overall network performance. 
strengths_and_weaknesses: Overall Review
===========
Overall, the authors present an interesting phenomenon in residual networks in an ingenious way (social loafing), while making use of that knowledge to improve such architecture. On the other hand, the experimental evaluation leaves many questions open (see below). In its current state, I think this work does not meet NeurIPS standards but I encourage the authors to continue working on it and to submit a rebuttal.

Strengths
=======
* The authors succeed at showing that "social loafing" does happen in residual networks and that fixing it results in a performance improvement.
* The experimental design succeeds at convincing the reader that there's loafing and that it can be fixed.
* The authors include a theoretical analysis and reproducibility details in the Appendix.

Weaknesses
=========
* Reading the submission generates a series of questions that are not answered by this work. For instance, does loafing happen in stochastic depth networks? In fact, it would be nice to stochastic depth networks as a baseline in all the figures. Can your method be applied to pre-trained neural networks? What if instead of the KL you used the same cross-entropy loss as in the output? 
* The authors of DenseNet [A] state that this kind of neural network is less redundant than resnet since each layer is aware of what the other layers are learning. It would be interesting if you checked whether DenseNet also suffers from loafing.
* The block dropping strategy is the same as in stochastic depth networks, thus I would devote less space to it in the paper and state that you use the same strategy.
* While stochastic depth networks reduce the training computation time, this work increases it. 

[A] Huang, Gao, et al. "Densely connected convolutional networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. 

Detailed comments
===============
Originality
------------
I found original the connection with social loafing. The method to solve it is a combination of multiple existing ideas but I think that it can be considered novel given the context in which it is introduced.

Quality
--------
The overall quality is good but the experimental quality could be improved. For example, you could explore if loafing happens or if it is solved with other methods such as stochastic depth or densenet.

Clarity
-------
The text is clear and easy to read. There are some minor typos.

Significance
--------------
Given the performance improvement introduced by the proposed method, it could be adopted by future work. A major drawback is the increase in computational cost due to having to compute two losses every time. 

Reproducibility
-----------------
The authors provide details to reproduce their work, however they do not provide the final code.
questions: * Does loafing happen in stochastic depth networks? In fact, it would be nice to stochastic depth networks as a baseline in all the figures.
* Can your method be applied to pre-trained neural networks? 
* What if instead of the KL you used the same cross-entropy loss as in the output? That might reduce the computational cost since you would be able to compute the subnetwork loss without the logits of the main network.
* It would be interesting if you checked whether DenseNet also suffers from loafing.
* If the block dropping strategy is the same as in stochastic depth networks, you could just cite them and use the space to introduce changes suggested by the reviewers.
* A main limitation is the additional computational cost, could you comment on it? Another option is to find a better strategy that does not increase it (e.g. keeping a buffer of previous logits so that you do not have to execute the main network every time).

**Minor**
* L48: The loafing problem.. inclines to be more severe -> is inclined to be
* L142: sub-networks.. only has -> only have
* L157: balanced coefficient -> balancing coefficient
* L161: To open and close quotes (") in latex use `` blabla '', this way they display properly in the text
* L34: unraveled view -> the unraveled view
limitations: There is no limitations section, I encourage the authors to add one discussing issues such as the computational cost or the applicability of their method beyond resnets (such as transformers).
review_title: null

Meta-review: metareview: This paper proposes to study the "loafing" problem in deep ResNets, which suggests that the sub networks of a deep ResNet perform significantly worse than the same architecture trained alone. It proposes a simple technique which jointly trains the main network and the KL divergence the main network's output and that of a random subnetwork. It is shown empirically that this technique improves the final accuracy for both the main network and random subnetworks. The reviewers agreed that the "loafing" problem is an interesting phenomenon, but also raised concerns about both the motivation/presentation and comparison with similar techniques like deep supervised and self distillation. The authors provided extensive responses with new additional experimental results. After the discussion phase, the reviewers reached to a consensus of acceptance, conditioned on that the authors carefully address the framing of "loafing" and make clear that the "loafing" term is just a loose analogy without any real implications to biology. The AC agrees that the problem identified in this paper is interesting and can have implications to both regularization and model compression. However the authors should try to remove the excessive reference to the social psychology aspects, which does provide scientific justification to the method but rather could invite unnecessary confusion and controversy. 
confidence: Less certain
Decision: Accept