Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: This paper tackles an important problem of neural network pruning. Specifically, the authors of the paper propose a novel method to prune coupled channels in neural networks. For instance, the layers with skip connections in the ResNet model are considered to be coupled channels. This is an under-considered problem, and most of the previously proposed pruning methods neglect these coupled channels. Nevertheless, coupled channels consist of a significant portion of many modern neural network architectures. in essence, the proposed approach involves traversing through all the paths between any pair of such couple channels, and aggregating the overall pruning scores for all of these individual paths. Then, an overall score can be determined and such couple channels can be effectively pruned. The authors of the paper conduct careful experiments to demonstrate the effectiveness of the proposed method. 
strength_and_weaknesses: Strength: 
- The problem considered is an important one, and the proposed method is technically sound. 
- Experiments conducted show that the proposed method achieves good empirical performance. 
- The paper is well written in general. 

Weaknesses:
- The proposed BGSC algorithm seems quite computationally expensive. While there is an analysis of the time complexity, it would be nice to also report the practical time taken to run the algorithm in practice. Does the proposed method take up significantly more time?
- I feel like the BGSC algorithm needs more explanation. While the overview gave a good insight into the algorithm, there are a lot of technical details in the algorithm that needs to be further discussed and elaborated. 
clarity,_quality,_novelty_and_reproducibility: In general, the paper is clearly written and enough details are provided to reproduce the experiments.
summary_of_the_review: All in all, I have some minor concerns regarding the computational cost of the proposed method. nevertheless, I think this is a good paper and think that it should be accepted.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper focused on coupling the data flow to outputs and inputs, allowing for interconnections between outputs of
multiple layers. This led into the concept to prune CC without data to obtain aster latencies.
strength_and_weaknesses: The algorithms and parameters were well outlined in this paper. In total, the contributions made in the paper were well set out.

More data visualisations could have been done to show the impact of this approach
clarity,_quality,_novelty_and_reproducibility: The article was well written, and the novelty well understood.
summary_of_the_review: Overall, this paper adds value to the body of knowledge.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper proposes a method to prune deep neural networks. The motivation of this work is to consider DATA FLOW DRIVEN PRUNING OF COUPLED CHANNELS  (DFPC), which considers the coupled channels (CCs) in a data-free mode when conducting network pruning. The key part of this work is developing a method called DFCs that abstracts the end-to-end transformation of CCs. Then, a Maximum Score Disagreement mechanism is proposed for group saliency for DFCs. Experiments on VGG and ResNets show promising results across various image classification datasets, including Cifar10/100 and ImageNet.
strength_and_weaknesses: Strengths
1. The paper targets the important problem of coupled channels for residual connections, which improves the performance of several classic networks.
2. The observation of disagreement for channel importance is interesting. Before this, metrics mainly used L1 or Taylor Approximate which doesn't take any channel dependency into account. 
3. The overall writing is easy to follow.

Weaknesses
1. The authors claim that their method aims to prune channels like those fed into residual connections. However, they only conduct experiments on residual networks, and other popular networks such as mobilenets, densenets have no comparative experiments.
2. It's hard to read the text and understand Figure4. 

clarity,_quality,_novelty_and_reproducibility: The paper is relatively novel enough for its proposed DFPC. The content is presented clearly and organized reasonably. If the authors provide the source code for this paper, it will be easier for the reproducibility
summary_of_the_review: Overall, I vote for borderline acceptance. Specifically, I liked the idea of investigating the disagreement mechanism among the coupled channels and proposing a new method for data-free pruning.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: This paper focuses on structured pruning of neural networks, with a particular focus on pruning "coupled channels" - activations that are connected in the neural network through residual connections. The paper proposes a data-free approach (i.e. not requiring additional fine-tuning or access to data) to identifying which coupled channels should be pruned. The method outperforms prior pruning methods in terms of speed and does not sacrifice too much accuracy. Reviewers all felt the paper was strong enough to warrant publication, particularly after the rebuttal. The authors should update their figures to make them readable, since the text is much too small.
justification_for_why_not_higher_score: The proposed method seems to be meaningfully better, but it is not a dramatic improvement. It's also a nice problem.
justification_for_why_not_lower_score: Reviewers all recommended acceptance. I am not super familiar with the literature on pruning (it's a total mystery to me why I keep getting assigned papers in this area) so it's possible it could be rejected. But I trust the reviewers here.
Decision: Accept: poster