From: ICLR.cc/2023/Conference/Program_Chairs
title: Paper Decision
decision: Reject
metareview:_summary,_strengths_and_weaknesses: The paper proposes a new structural filter pruning method, namely Cluster Pruning (CP), which replaces the redundant filters with cluster centroids. Furthermore, it removes the unimportant filters using the proposed channel addition operation. This alleviates the need for extra fine-tuning stage. While the problem is well-motivated and the proposed method is of potential, there are concerns about the superiority of the proposed method to existing baselines, lack of comparison to SOTA baselines, and missing experimental details. There are good references mentioned by reviewers ddXD and hd69, which the authors should discuss and add as baselines. The paper also benefits from ablation study on the choice of hyperparameters, and reporting optimization time as well as the number of remaining weights in the network.


justification_for_why_not_higher_score: Explained in part 1.
justification_for_why_not_lower_score: N/A

From: ICLR.cc/2023/Conference/Paper925/Reviewer_uzoq
confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
summary_of_the_paper: This paper deals with the problem of structural filter pruning and proposes a new method called Cluster  Pruning (CP). CP replaces the redundant filters with cluster centroids. In this way, CP can preserve the representational power because no filters are actually pruned during training. Meanwhile, unimportant filters could be removed by using the proposed channel addition operation, getting rid of the need for an extra fine-tuning stage. Experiments on CIFAR10 and ImageNet are conducted to validate the proposed technique.
strength_and_weaknesses: **Strength:**

1. Based on the proposed stochastic pruning scheme, the full channel space is preserved during the training process. It is of wisdom to replace redundant filters with cluster centroids.

2. Alternating three steps of Clustering, Selection, and Recovery Step can identify unimportant filters. Moreover, Channel Addition module can prune channels while preserving the performance.

3. The effectiveness of the proposed method is validated by extensive experiments on various network architectures and ablation studies.

 **Weaknesses:**

1. The training efficiency of the proposed method is not discussed. It is seen that CP needs to perform clustering for all layers and retraining in recovery step. Will CP require additional training epochs, compared with other channel pruning methods (both methods w/ and w/o finetuning? Since the recovery step needs to retrain the network for stability of training which works like finetuning, CP cannot be rigorously thought as a filter pruning method without finetuning.

2. The motivational visualization is not very clear. For example, Fig.1a&c only show the similarity between feature maps in some layers. It is important to visualize clustering effects in more layers, as filter pruning is conducted across the network. Besides, the PCA visualization in Fig.1c does not necessarily present the clustering effect in filters.

3. The hyperparameter $c_i$ is critical for the final performance and FLOPs reduction. But Eqn.(9) is not well explained. What does it mean `scale the pruning rate to the worse case’?
Moreover, the pruning rate $P_r$ should be measured by the ratio of FLOPs of the original network and the pruned network. But it seems that $P_r$ in Eqn.(9) is related to the number of pruned filter. The Algorithm 1 does not explain how to obtain $c_i$ according to Eqn.(9). Furthermore, both $c_i$ and $P_i$ affects the final pruning ratio, could the authors provide principle way to tune these layer-wise parameters.

4. The empirical results are not strong. The paper is not sufficiently well explored for inclusion in the conference. For example, CP achieves 0.05 accuracy gain while reducing 37.1% FLOPs on VGG-16. But SCP [1] achieves 0.35 accuracy gain while reducing 51.4% FLOPs on VGG-16. Moreover, the baseline accuracy of ResNet-50 in CP on ImageNet is a bit lower than the other pruning methods, which makes the comparison unfair.

clarity,_quality,_novelty_and_reproducibility: The paper investigates channel pruning by exploring the clustering effects in filters, which is somewhat novel to my knowledge. This work may be hard to reproduce as the code is not given and some hyperparameters are not specified.
summary_of_the_review: In summary, I think this is a borderline paper. It has some good properties, but it is limited by using clustering and retraining and less competitive results. It also introduces additional costs at the training time.
correctness: 3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.
technical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
empirical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
flag_for_ethics_review: NO.
recommendation: 5: marginally below the acceptance threshold

From: ICLR.cc/2023/Conference/Paper925/Reviewer_8kEQ
confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
summary_of_the_paper: In this paper, authors propose Cluster Pruning (CP), a filter pruning method based on clustering. Different from removing or zeroing out unimportant filters, the proposed method intends to use the equivalence of the convolutional layer to remove filters without impact. Extensive experiments on two datasets demonstrate that the proposed CP can achieve the best trade-off between complexity and performance compared with other state-of-the-art methods.
strength_and_weaknesses: Strength:
Pruning models by clustering is an interesting idea, where redundant filters are replaced by similar filters to reduce model complexity. And in the ImageNet on CIFAR-10 datasets, this method makes a good tradeoff between cost and accuracy.

Weaknesses:
This approach relies heavily on hyperparameter settings, such as the pruning rate or the number of clusters per layer. And the solution proposed by the authors is too intuitive to be supported. The so-called adaptive coefficient does not show true adaptation; in other words, it still depends on a set of already established rules, such as pruning rate and the N_out. 
clarity,_quality,_novelty_and_reproducibility: The writing is clear and easy to understand. Novelty is up for debate and the authors provide some details but no code.
summary_of_the_review: Actually, I'm not an expert in this area, but I still find some problems. Technically, the core of this method should be to design a more intelligent hyperparameter selection algorithm for a better balance. On the contrary, the proposed method is too intuitive and common, and lacks technical innovation. Therefore, I recommend a score of 5 for this paper.
correctness: 3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.
technical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
empirical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
flag_for_ethics_review: NO.
recommendation: 5: marginally below the acceptance threshold

From: ICLR.cc/2023/Conference/Paper925/Reviewer_ddXD
confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
summary_of_the_paper: The authors propose a cluster-based filter pruning method to prune CNNs by considering the similarity between filters. For each layer, they propose to cluster similar filters and assign the pruned filters to the nearest cluster in an iterative manner. Then, the authors introduce channel addition operation to remove the identical filters and thus reduce the computational complexity of models. Results on CIFAR-10 and ImageNet demonstrate the effectiveness of the proposed method. However, the novelty and contributions of this work are limited.   
strength_and_weaknesses: ## Strength:
1. The authors propose a filter pruning scheme based on filter clustering and introduce adaptive coefficients to choose the number of clusters.
 
2. The authors study the impact of different clustering methods and prove that the proposed pruning scheme is robust to the clustering methods.

3. Experiments on two datasets including CIFAR-10 and ImageNet show improved performance compared to prior work.

## Weakness:
1. In Abstract, the authors claim that previous studies suffer from two comment limitations: 1) directly removing redundant filters results in performance degradation; 2) fine-tuning is required to recover model performance, which is time-consuming. However, both issues have been addressed by existing methods, such as [1][2].

2. The authors propose to measure the filter redundancy from the perspective of similarities between filters, which is similar to some existing work such as [3][4][5].

3. The authors claim that “The cost in time and the need for training data make it difficult to deploy in real-world scenarios”. However, the proposed method also requires training data for training. Besides, the efficiency of the proposed method is not clear. It would be better to provide the training time under different settings.

4. To evaluate the similarity of filters, another useful metric is Cosine Similarity. It would be better to discuss and compare the impact of different similarity metrics on the selection step.

5. The choose of the hyper-parameters $k_1$ and $k_2$ is trivial. More analysis on the impact of these hyper-parameters is needed.

6. The motivation to use the same pruning rate across layers and thus simplify Equation (9) is not clear. In fact, existing studies have proved that an adaptive pruning rate is required to obtain more compact models with better performance [6][7][8].   

7. In Table 3 and Table 4, the optimization time is required to further compare the efficiency of different strategies.

8. The authors only perform filter pruning on models with heavy weights (e.g., ResNet or VGG). It would be better to adopt the proposed method on more compact models such as MobileNet-V2[9]. 

9. The reduction of parameters is also an important metric for pruning methods. It would be better to show the number of parameters of pruned models in Table 1 and Table 2.

### Reference:
[1] Asymptotic soft filter pruning for deep convolutional neural networks. IEEE Transactions on Cybernetics. 2020.

[2] An efficient channel-level pruning for CNNs without fine-tuning. IJCNN. 2021.

[3] Centripetal SGD for pruning very deep convolutional networks with complicated structure. CVPR. 2019.

[4] Redundant feature pruning for accelerated inference in deep neural networks. Neural Networks. 2019.

[5] Filter clustering for compressing CNN model with better feature diversity. IEEE Transactions on Circuits and Systems for Video Technology. 2022.

[6] Amc: Automl for model compression and acceleration on mobile devices. ECCV. 2018.

[7] Metapruning: Meta learning for automatic neural network channel pruning. ICCV. 2019.

[8] Discrimination-aware network pruning for deep model compression. TPAMI. 2021. 

[9] MobileNetV2: Inverted residuals and linear bottlenecks. CVPR. 2018.

clarity,_quality,_novelty_and_reproducibility: The manuscript is easy to read. However, there are some issues needed to be addressed to improve the quality and novelty of this work. The reproducibility is not clear as the design of the pruning strategy is tricky. Please refer to the detailed comments.
summary_of_the_review: The novelty of the proposed method is not clear as the idea of filter cluster has been explored by existing works. Besides, the issues claimed by the authors also have been considered in previous SFP-based methods, which degrades the contributions of this work.
correctness: 2: Several of the paper’s claims are incorrect or not well-supported.
technical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
empirical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
flag_for_ethics_review: NO.
recommendation: 5: marginally below the acceptance threshold

From: ICLR.cc/2023/Conference/Paper925/Reviewer_hd69
confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
summary_of_the_paper: This work proposes a new filter pruning method called Cluster Pruning (CP). In particular, it clusters similar filters into several and replaces these filters with their corresponding centroids. To avoid incorrect assignment, a recovery stage is introduced to re-train these filters. Experimental results on several popular datasets are conducted to verify the effectiveness of the work.
strength_and_weaknesses: The strengths of this work are listed as below:
+ The paper is well organized and easy to follow.
+ Extensive experiments are conducted to verify the effectiveness of the work.
+ Extensive ablations are conducted to analyze the components and hyper-parameters of the work.

The weaknesses of this work are listed as below:
- There are some important details missing. For example, the overall objective is not explained. 
- The writing can be improved. There are some typos below: (1) "operation operation" in caption of Figure 2 is a typo; (2) in Figure 4(b), "Number of Cluster" should be "int" instead of "%";
- The motivation of filter clustering is not strong for me. Replacing the filter with its centroid and zeroing it out are equivalent, since the filter provides no extra information. Therefore, why not re-train a pruned network via commonly used zeroing out strategy instead of the proposed clustering strategy. It should be further discussed.
- There are some important SOTAs missing in comparisons. The followings are some samples, some of them seem to show better performance than the proposed method: 
(1) Ding, Xiaohan, et al. "Resrep: Lossless cnn pruning via decoupling remembering and forgetting." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021. 
(2) Li, Bailin, et al. "Eagleeye: Fast sub-net evaluation for efficient neural network pruning." European conference on computer vision. Springer, Cham, 2020.
(3) Ruan, Xiaofeng, et al. "DPFPS: dynamic and progressive filter pruning for compressing convolutional neural networks from scratch." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. No. 3. 2021.
clarity,_quality,_novelty_and_reproducibility: The clarity and novelty are good.
This work provides detailed settings and hyper-parameter analysis to make sure that the work can be reproduced. However, pseudo-codes for adaptive coefficient are recommended.
summary_of_the_review: This work proposes a new filter pruning method and conducts experiments to verify the effectiveness. Though the work is easy to follow, some important SOTAs are missing during comparisons. Therefore, the experimental results are not convincing enough. On the other hand, the motivation of the clustering should be further discussed. 
correctness: 3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.
technical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
empirical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
flag_for_ethics_review: NO.
recommendation: 5: marginally below the acceptance threshold