$$CONVOLUTION AND POOLING OPERATION MODULE WITH ADAPTIVE STRIDE PROCESSING EFFEC$$
Authors: 雪枫 刘
志勇 林
Venue: Submitted to ICLR 2023 (2023)
Keywords: convolution
pooling
adaptive
stride

Abstract: $$Convolutional neural network is one of the representative models of deep learning,
which has a wide range of applications. Convolution and pooling are two key op-
erations in convolutional neural networks. They play an important role in extract-
ing input features and mapping low-level semantic features to high-level semantic
features. Stride is an important parameter involved in convolution and pooling
operations, which refers to the distance of each slide of the convolution kernel
(pooling kernel) during the convolution (pooling) operation. The stride has an
impact on the granularity of feature extraction and the selection (filtering) of fea-
tures, thus affecting the performance of convolutional neural networks. At present,
in the training of convolutional neural networks, the content of convolution ker-
nel and pooling kernel can be determined by the optimization algorithm based
on gradient descent. However, the stride usually cannot be treated similarly, and
can only be selected manually as a hyperparameter. Most of the existing related
works choose a fixed stride, for example, the value is 1. In fact, different tasks
or inputs may require different stride for better model processing. Therefore, this
paper views the role of stride in convolution and pooling operation from the per-
spective of sampling, and proposes a convolution and pooling operation module
with adaptive stride processing effect. The feature of the proposed module is that
the feature map finally obtained by convolution or pooling operation is no longer
limited to equal interval downsampling (feature extraction) according to a fixed
stride, but adaptively extracted according to the changes of input features. We ap-
ply the proposed module on many convolutional neural network models, including
VGG, Alexnet and MobileNet for image classification, YOLOX-S for object de-
tection, Unet for image segmentation, and so on. Simulation results show that the
proposed module can effectively improve the perform$$

Under review as a conference paper at ICLR 2023
CONVOLUTION AND POOLING OPERATION MODULE WITH ADAPTIVE STRIDE PROCESS- ING EFFECT
Anonymous authors Paper under double-bling bbind review
ABSTRACT
Convolutional neural network is one of the representative models of deep learning, which has wide range of applications. Convolution and pooling are two key op- erations in convolutional neural networks. They play an important role in extract- ing input features and mapping low-level semantic features to high-level semantic features. Stride is important parameter involved in convolution and pooling operations, which refers to the distance of each slide of the convolution kernel (pooling kernel) during the convolution (pooling) operation. The stride has impact on the granularity of feature extraction and the selection (filtering) of fea- tures, thus affecting the performance of convolutional neural networks. present, in the training of convolutional neural networks, the content of convolution ker- nel and pooling kernel can be determined by the optimization algorithm based on gradient descent. However, the stride usually cannot be treated similarly, and can only be selected manually as a hyperparameter. Most of the existing related works choose fixed stride, for example, the value is 1. In fact, different tasks or inputs may require different stride for better model processing. Therefore, this paper views the role of stride in convolution and pooling operation from the per- spective of sampling, and proposes a convolution and pooling operation module with adaptive stride processing effect. The feature of the proposed module is that the feature map finally obtained by convolution or pooling operation is no longer limited equal interval downsampling (feature extraction) according stride, but adaptively extracted according to the changes of input features. We ap- ply the proposed module on many convolutional neural network models, including VGG. Alexnet and MobileNet for image classification, YOLOX-S for object de- tection, Unet for image segmentation, and so on. Simulation results show that the proposed module can effectively improve the performance of existing models.
fixed
INTRODUCTION
The research on convolutional neural networks started from 1980s 1990s. Time delay network and LENET-5 were the earliest convolutional neural networks. After the 21st century, with the proposal of deep learning theory and the improvement of computing equipment, convolutional neural net- works have developed rapidly and been applied in computer Aision(Krizhevsss et al. 2012), natural language processingQiuuqiant Kong 2020) and other fields.
Operators in convolutional neural network include convolution operator and pooling operator. The elements of the convolution operator include the size of the convolution kernel, the numerical size of the convolution kernel, the stride of the convolution operation and so on. The elements of the pooling operator include stride, padding, and so on. Convolution neural network to point to: convolution kernels pooling operator acting on by convolution or by pooling area, convolution kernels or pooling operator each sliding distance, convolution and pooling operation is to extract the characteristics of the input and the lower sampling, stride for feature extraction of the characteristics of grain size and trade-off (filtering), which influence the properties of convolution neural network. In the current convolutional neural network, the stride of convolution pooling operator convolution or pooling operation is manually selected as a hyperparameter and is fixed. The fixed stride means that the sliding distance of convolution kernel (pooling kernel) is the same
the stride

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Under review as a conference paper at ICLR 2023
each time, and the fixed stride is which will cause redundancy in extracting features. Different tasks may require different stride, for example, the pool operation stride is fixed to 2, and fixed to 2 is not fixed to 1, and may cause the loss of features. Moreover, the stride directly obtained by learning added to the model can not he backpropagated in training to update.
Self-adaptation is the process of automatically adjusting the processing method, processing quence, processing parameters, boundary conditions or constraint conditions according to the data characteristics of the processed data in the process of processing and analysis, so that it can adapt to the statistical distribution characteristics and structural characteristics of the processed data, so as to achieve the best processing effect. The introduction of self-adaptation in convolutional neural network can make the network more adaptable to input changes, so as to make the whole network play better effect, such as the improvement of processing speed, accuracy and recall. At present, adaptation in convolutional neural networks includes adaptation in determining the depth of con- volutional operators Veit & Belongie| 2019), adaptation determining the width of convolutional operators[Jagg Ma. 2018), and adaptation in determining the parameters of convolutional neural networks (Adam W. Harley 2017), Adaptation in determining the receptive field of convolution
operator Xizhou Zhu 2019), pooling operator determination Yu Lee 2013). etc.
Since the stride obtained by direct learning cannot be backpropagated to update the stride when added to the model, it cannot be determined by the optimization algorithm based gradient descent, and can only be selected manually as a hyperparameter. Therefore, the current academic research on the stride adaptation in convolutional neural networks is basically blank. (Rach.d Riad, 2022) proposed an adaptive stride method for images in the frequency domain, and obtained the stride by learning method filter out high-frequency signals in the frequency domain. However, the learning stride is different from the stride concept in traditional convolutional neural networks. In this paper, a convolution and pooling operation module with adaptive stride for traditional convolution operation, namely CAS(Convolution operation adaptive stride) and PAS(Pooling operation adaptive stride) module, is proposed.
We find that in the traditional convolution or pooling operator (operation), fixed convolution or pooling stride S (S>1) The convolution or pooling operation results obtained can essentially be regarded as those obtained by adopting convolution then taking down-sampling at equal intervals (that is, discarding or suppressing part of the results). Based on the insight, further promotion, in order to obtain more general convolution with adaptive stride or pooling operation effect, can consider to use stride convolution or pooling of input feature maps are operation, obtain the preliminary convolution operation pooling results (initial feature maps), and then trying to based on the input features to generate a suitable mask characteristic figure, The mask feature map is combined with the initial feature map, and some results discarded or suppressed (for example, insignificant results are set to 0), so as to obtain the final feature map with adaptive stride convolution or pooling operation effect. The most important aspect of adaptation is that the feature map finally obtained by convolution or pooling operation is no longer limited to equal interval downsampling or feature extraction according to a fixed stride S, but adaptively extracted according to the change of input features.
pooling operation results with stride I and
METHODS
In convolutional neural networks, convolution operation or pooling operation can extract features and map the underlying semantic features higher-level semantic features and the role of down- sampling. Classification needs local features and localization needs global features. In convolutional neural network, the convolution or pooling operation stride fixed as which can extract detailed features, but will cause feature redundancy. Because the position of the object in each picture is changed, the stride sliding distance the same each time but not fixed to may cause the loss of features. Therefore, it is the correct way to the stride in convolution pooling operation to adaptively determine the stride for each movement process of convolution kernel and pooling oper- ator according to the input feature map. It is hoped that convolution or pooling operation can focus on the key areas needed.
This is slightly different from the traditional concept of fixed stride, which means that the convolu- tion kernel or pooling operator moves the same distance every time. As shown in Figure 1, the red region is the convolved pooled region.
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(a) The stride is 1
(b) The stride is fixed and the stride is larger than 1
(c) The adaptive stride
Figure 1: Convolution or pooling effect with different stride
2.1 CAS MODULE
2.1.1 RELATIONSHIP BETWEEN THE SAMPLING
The stride of the convolution operation refers to the distance that the convolution kernel slides each convolution. Figure 2,3 illustrates the convolution operation with stride and stride 2 and compares results.
(a)
(b)
Figure 2: Stride is
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(a)
(b)
Figure 3: Stride is
Figure shows the comparison convolution results with stride and stride The left is the result with stride 1, the right is the result with stride 2, and the middle shows the relationship between them.
40
55
56
46
46
56
46
Figure 4: Comprision bwteen different stride result
It can be that the fixed convolution stride S (S>1), the convolution operation results obtained can be essentially regarded as those obtained by taking equal interval downsampling on the basis of convolution operation results with stride
2.1.2 MASK FEATURE MAP
In order to obtain the result of convolution operation with stride not 1, we can sample the result feature map of convolution operation with stride 1. In order to realize sampling, we introduce the concept of mask feature map. The size of the mask feature map is the same as that of the convolution operation with stride 1. There are some zeros and some positive numbers the mask feature map, and the positions of the non-zero positive numbers equally spaced. Then, the mask feature map and the result feature map convolution operation with stride are used for the bitwise multiplication processing, as shown Figure 5.
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fastrr napwith selection and sttumtonn adtiiz medoonss
120
45
obcain
Multioly the
Feetize
Figure 5: Through the mask feature map sampling process
The mask feature map is processed by bitwise multiplication with the resulting feature map of con- volution with stride 1. The position of the mask O corresponds to the volume of stride 1 and the position of the feature map of the operation result. Set this parameter 0. The position selection corresponding to the positive number is retained, and the attention mechanism is added. The value at the position
will not continue to propagate forward and can be regarded as blank, which is similar the size change of the feature map resulting from the convolution operation whose stride not in the traditional concept.
2.1.3 ADAPTIVE STRIDE
As shown Figure 6, the positions of non-zero positive numbers in the mask feature map are not equally spaced. The adaptive stride method proposed in this paper is no longer limited to fixed convolution stride (S>1) 1), but adaptively sampling the convolution operation result with stride according to the input feature map, which can make the convolution operation focus more the important information of the feature map. The position of in the target feature map is regarded as the convolution operation stride of this position plus one, and the position of the color box is the actual convolution area, as shown in Figure 6.
Mask feature mapwith selectio and sttention adding mediorion
3
Multinly bin
3
5
2
2
1
6
47
45
9
55
55
actual convolution
46
45
Fosturn map with strita
Figure 6: Through the mask feature map sampling process
2.1.4 ADAPTIVITY GENERATED BY THE MASK
The generation of the mask feature map is obtained through the three-layer convolutional layer volution processing of the feature map with the result step in the convolutional neural network. In addition, the mask feature map is processed with activation transformation (such Relu, etc.)
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to add selection effect and attention mechanism effect the mask feature map. The reason for lecting the three-layer convolutional layer is to generate the feature map with the same size as the feature map resulting from the convolution operation with step stride of 1. At the same time, the three-layer convolutional layer is regarded as a complex function(lifeng Dail 2017). and the mask feature map is generated adaptively according to the input feature map. The reason for choosing Relu is that the Relu function can set the position required by the mask feature map to O, and can carry out back propagation in the training.
2.1.5 THE CAS OVERALL METHOD
The overall method of CAS is as follows: with fixed stride, and the initial feature map generated. The input feature map is convolved by three convolutional layers, and the mask feature map is generated. The mask feature map was processed with activation transformation (such as Relu(Glorot X 2010), etc.), and selection action and attention mechanism were added to the mask feature map. The mask feature map is multiplied by the initial feature map to generate the target feature map, and the convolution operation result with adaptive stride effect is obtained, shown in Figure 7.
The input feature map is convolved by convolution layer
Ralu
Mask feature map
Convolutionnn
larget feature map
Input festure map
feature
Figure Through the mask feature map sampling process
2.2
PAS MODULE
The function of pooling layer is feature extraction and downsampling, and its stride is usually de- termined by cross validation. The overall method of PAS is as follows: The input feature map is pooled by pooling layer with fixed stride, and the initial feature map is generated. The input feature map is convolved by three convolutional layers, and the mask feature map is generated. The mask feature map processed with activation transformation (such Relu X 20T0), etc.), and the selection action and attention mechanism were added to the mask feature map. The target feature map is generated by the bitwise multiplication of the mask feature map and the initial feature map, and the pooling result with the effect of adaptive stide is obtained.
EXPERIMENT
We carried out the convolution operation in the classification network, object detection network, segmentation network adaptive stride experiments. The adaptive stride experiment of pooling oper- ation on classification network also carried out. The classification accuracy has been improved, and visual experiments have been done to verify the effectiveness and generality of our method.
Through experiments, we find that the adaptive stride plays a role in extracting local features. Clas- sification task is the need for local characteristics, the more the farther the back-end network char- acteristic figure network own extract local features, but there is interference and limited data set, the partial feature of the network itself extraction is not perfect, our CAS module should be

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Classification network selected classic classification networks VGG, Alexnet and MobileNet, all of which added CAS module in the middle and front of the network, Alexnet is shown in Figure Under review as a conference paper at ICLR 2023
added in the network of the middle and front, it can better help the network to extract local features. Moreover, we found that CAS module can be added to the middle and front of the object detection model network segmentation network, because the localization needs relatively global features, and the adaptive stride helps extract local features to improve both classification and localization. also be added to the front of the classification decoupled branch of the object detection model with decoupled classification localization task, because the decoupled stem layer is global feature.
The accuracy has been improved, as shown in Table For object detection network, YOLOX-S network with decoupling of classification and positioning is selected, and CAS module is added to the stem layer with decoupling of classification and positioning, as shown in Figure 8. The accuracy has been improved, as shown in Table 2. We selected Unet for the segmentation network, and added CAS module to the subsampling convolution layer at the second layer the network. Both accuracy and mIU have been improved, indicating that CAS module is helpful for classification and positioning, as shown in Table We selected VGG and Alexnet for PAS module, and the model accuracy was improved, shown in Table 4.
3.1
EXPERIMENT ON ADAPTIVE SRIDE OF CONVOLUTION OPERATION
3.1.1
CLASSIFICATION OF NETWORK
Because of the overlap of Trick effect, we chose the most original version of VGG network and Alexnet the effect shown in Table 1.
Relu
Conv
Figure 8: Alexnet adds adaptive stride
Table 1: Classification of network
Model
Dataset
Acc
CAS module Acc
VGG
Cifar10
55.11%
55.83%
Alexnet
C1
68%
70%
MobileNet
Cifar10
90.680%
91.840%
Through the experiment, it is found that adding CAS module the classification network can help the network to better extract local features and improve the classification accuracy.
3.1.2 OBJECT DETECTION
Localization needs global features while classification needs local features. The YoloX-S model decouples the classification task from the localization task. Because the stem layer is used for both classification and localization, a global feature. Our module is applicable to the classification network and the front-end of the network, so the YOLOX-S model, CAS module is added

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to the stem layer and the first layer of the classification branch connection. This is shown in Figure 9,and the effect is shown in Table2.
OOLOX Decoupted Head
Ch.
WwddWi
Regg
#xWx256
Tull
Figure 9: YOLOX with CAS
Table 2: YOLOX-S
Dataset
condition
YOLOX-S AP
CAS module AP
COCO
Iou=0.50:0.95 arca=all maxDets=100
40.4%
41.0%
COCO
Iou=0.50 area=all maxDets=100
59.2%
59.6%
COCO
1ou=0.75 area=all maxDets==100
43.6%
44.5%
COCO
lou=0.50;0.99 area=small max
23.2%
22.6%
COCO
1ou=0.50;0.99 area=medium maxDets=100
44.5%
45.3%
COCO
lou=0.50;0.95 area=largel maxDets=100
53.1%
54.3%
3.1.3 SEGMENTATION NETWORK
Positioning is relatively global feature, and our CAS module is also helpful for the extraction of relatively global features, so we add the CAS module in the middle and front of Unet. was found that both classfication and positioning were improved,as is shown in Table 3.
Table 3: Unet
Model
Dataset
Acc
Acc cls
Mean iu
fwavacc
original
Unet
Voc
74.40% 75.02%
14.70% 18.74%
10.75% 13.99%
58.71%
CAS
Unet
Voc
59.58%
3.2 EXPERIMENT OF ADAPTIVE STRIDE OF POOLING OPERATION
In the experiment of the adaptive step size of pooling operation, we chose the original VGG, Alexnet network and Alexnet network with many tricks added, and added the PAS module in the second pooling layer. The original step size of pooling layer was selected as 2. The improvement obtained shown in Table 4, which verifies the effectiveness of the adaptive stride the pooling layer.
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Table 4: PAS experiment
Model
Dataset
Acc
PAS
VGG
Cifar10 55.11% 1%
56.23%
Alexnet
Cifar10 68%
71%
Alexnet with tricks
Cifar10 74.140%
77.583%
3.3 VISUALIZATION EXPERIMENT
In the visualization experiment, output the original image and mask for comparison, shown in Figure 10. The left the original image and the right is the mask. The experimental results show that the position of non-zero positive number in the mask is exactly the position of the object, which verifies our idea of extracting local features with adaptive stride.
(c)
(d)
Figure 10: Visualization experiment
4 CONCLUSION AND FUTURE WORK
We design an adaptive stride module in convolutional neural network, which verifies the effective- ness and generality our method in many models. The adaptive stride module does play a role in extracting local features, making the convolution operation or pooling operation more focused on the required features. The effect of the adaptive stride is derived from the mask feature map and the result feature map with stride is selected. The mask is generated by three convolutional layers and processed by activation transformation. In the future work, we will explore more mask generation methods and find other modules with adaptive stride.

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Under review as a conference paper at ICLR 2023
REFERENCES
lasonas Kokkinos Adam W. Harley, Konstantinos G. Derpanis. Segmentation-awal convolutional networks using local attention masks. 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2, 2017.
Zhuowen Tu Chen- Yu Lee, Patrick W. Gallagher. Generalizing pooling functions in convolutional neural metworks:mixed, gated, and tree. 1XXX::150900888512 [stat.ML] 10 Oct, pp. 2, 2015.
Bengio Y Glorot X, Bordes A. Deep sparse rectifier networks. Learning/stats Optimisation, pp. 6, 2010.
Xinyang Yi Jilin Chen Lichan Hong Ed H. Chi Jiaqi Ma, Zhe Zhao. Modeling task relationships in Proceedings of the 24th ACM SICKDD
multi-task learning with multi-gate mixture-of- International Conference on Knowledge Discovery Data Mining KDD, pp. 2018.
Yuwen Xiong Li Guodong Zhang Han Hu Yichen Wei Jifeng Dai, Haozhi Qi. Deformable convolutional networks. urXiv:1703.062. 1v3 [cs.CV] 5 Jun 2017, pp. 6, 2017.
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Advances in neural information processing systems. Curran Associates, Inc., 25:1, 2012.
Turab Iqbal Yuxuan Wang Wenwu Wang Mark D Plumbley Qiuqiang Kong, Yin Cao. Advances in neural information processing systems, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28:1, 2020.
Grangier D et al Rachid Riad, Teboul O. Learning strides in convolutional neural networks. PD- 2, 2022.
Andreas Veit and Serge Belongie. Convolutional networks with adaptive inference graphs. In Inter- national Journal of Computer Vision, pp. 2. 2019.
Stephen Lin ifeng Dai Xizhou Zhu, Han Hu. Deformable convnets v2: More deformable, better results. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2, 2019.
10


Reviews:
[{"review_id": "EdFAv02BJ-", "reviewer": "Reviewer_9Nmu", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "\"We find that in the traditional convolution or pooling operator (operation), a fixed convolution or\npooling stride S (S>1) The convolution or pooling operation results obtained can essentially be\nregarded as those obtained by adopting convolution or pooling operation results with stride 1 and\nthen taking down-sampling at equal intervals (that is, discarding or suppressing part of the results).\"", "summary_of_the_review": "If this paper is generated by AI then it is really a good job. If this paper is written by a real human, then it is its weakness. The paper is filled with trivial facts and no innovation.\n\nThe modules in the paper seem to be trivial special cases of off-the-shelf tools such as the deformable convolution. The experiments are almost nothing.", "strength_and_weaknesses": "If this paper is generated by AI then it is really a good job. If this paper is written by a real human, then it is its weakness. The paper is filled with trivial facts and no innovation.\n\nThe modules in the paper seem to be trivial special cases of off-the-shelf tools such as the deformable convolution. The experiments are almost nothing.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "1: The main claims of the paper are incorrect or not at all supported by theory or empirical results.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "Not applicable"}, "extra_text": {}}, {"review_id": "95YA2Ol3jyR", "reviewer": "Reviewer_3ok7", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "While a lot of CNN architectures are based on a fixed stride, this paper analyzes convolutional operations with an adaptitive stride. They developed a module that extract feature map by convolution with a stride that changes adaptively according to the changes of input features.\n\nThey apply the proposed module on many convolutional neural network models such as VGG, Alexnet, MobileNet, YOLOX-S and UNET. Simulation results are also provided to show the efficiency of the module.\n", "summary_of_the_review": "This paper raises the question of adaptive stride in convolutional neural network. To my knowledge, this is something new and. Even if the topic of this paper is very interesting, the authors still have a lot of work to do, particularly on the mathematical modeling of the problem and in the writing of the paper. ", "strength_and_weaknesses": "The topic of this paper is very interesting and the main strength relies on its technical novelty, since the authors propose a module based on a dynamic stride for convolution operation. However, this paper lacks mathematical models that can help to understand difference between fixed and dynamic strides. \n\nWhen using a dynamic stride, it is not clear if the size of the model (number of parameters will change) or the speed (number of operations). Could you discuss also this question ?", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "Not applicable"}, "extra_text": {}}, {"review_id": "6rV8NxWMkU", "reviewer": "Reviewer_DpD2", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "Authors introduce a method to learn strides by backpropagation.", "summary_of_the_review": "This paper needs to be rewritten entirely and better experiments realized (more details, more baselines) to convince the reader that the method is worth. I also would appreciate that authors explain how downsampling is actually performed, because otherwise the title and claims are misleading.", "strength_and_weaknesses": "Strengths:\n * Learning strides by backpropagation is still an open question as previous work that address this task suffer from a significant increase in computation cost compared to standard strided convolutions.\n\nWeaknesses:\n* The paper needs a complete rewriting, see points below:\n  * The paper is very hard to read, with unfinished sentences, useless sentences (eg. the first paragraph of the introduction should just be removed) and many grammar and syntax errors. An example is the first paragraph of the Methods section which I just do not understand.\n  * The total absence of notations and model details only provides the reader with a vague and abstract idea of the method.\n  * Figures 2 and 3 take almost a page just to illustrate the basic mechanism of striding, which is a waste of space.\n  * Many typos such as \"comprision\" in Figure 4, \"Classification of network\" as a title Section 3.1.1.\n  * Paragraph 2.2 is just a rewriting of paragraph 2.1.5, with copy-pasted sentences (!)\n* The method in itself is very questionable. First, unless I missed it, the method seems to only mask the target feature map leaving zero entries, which means that regardless of the \"adaptive stride\" learned by the model, there is no actual striding applied and the spatial dimensions remain constant in the entire network. This is completely different from striding, which effectively downsamples an image such that kernels of upper layers process inputs at lower resolutions. Moreover, the method suffers from the same limitation of [DiffStride](https://arxiv.org/abs/2202.01653)  as it first needs to compute an unstrided convolution + a mask produced itself by a convnet, which is overall much more costly than a simple strided convolution. This observation coupled to the fact that the model does not actually downsample but rather keep the spatial dimensions constant along the network lets me think that this model is much slower than baselines.\n* Not comparing to DiffStride and claiming that \"the current academic research on the stride adaptation [...] is basically blank\", the absence of confidence intervals, the lack of details on the experimental setting and the absence of time/space complexity analysis makes it impossible to evaluate the method in the current state.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject