Title: Model LEGO: Creating Models Like Disassembling and Assembling Building Blocks
Venue: NeurIPS 2024
Accepted: yes
Keywords: Disassembling Models

Assembling Models

Model Reuse

Model Interpretability
Abstract: With the rapid development of deep learning, the increasing complexity and scale of parameters make training a new model increasingly resource-intensive. In this paper, we start from the classic convolutional neural network (CNN) and explore a paradigm that does not require training to obtain new models. Similar to the birth of CNN inspired by receptive fields in the biological visual system, we draw inspiration from the information subsystem pathways in the biological visual system and propose Model Disassembling and Assembling (MDA). During model disassembling, we introduce the concept of relative contribution and propose a component locating technique to extract task-aware components from trained CNN classifiers. For model assembling, we present the alignment padding strategy and parameter scaling strategy to construct a new model tailored for a specific task, utilizing the disassembled task-aware components.
The entire process is akin to playing with LEGO bricks, enabling arbitrary assembly of new models, and providing a novel perspective for model creation and reuse. Extensive experiments showcase that task-aware components disassembled from CNN classifiers or new models assembled using these components closely match or even surpass the performance of the baseline,
demonstrating its promising results for model reuse. Furthermore, MDA exhibits diverse potential applications, with comprehensive experiments exploring model decision route analysis, model compression, knowledge distillation, and more.

Model LEGO: Creating Models Like Disassembling and Assembling Building Blocks

Abstract

With the rapid development of deep learning, the increasing complexity and scale of parameters make training a new model increasingly resource-intensive. In this paper, we start from the classic convolutional neural network (CNN) and explore a paradigm that does not require training to obtain new models. Similar to the birth of CNN inspired by receptive fields in the biological visual system, we draw inspiration from the information subsystem pathways in the biological visual system and propose Model Disassembling and Assembling (MDA). During model disassembling, we introduce the concept of relative contribution and propose a component locating technique to extract task-aware components from trained CNN classifiers. For model assembling, we present the alignment padding strategy and parameter scaling strategy to construct a new model tailored for a specific task, utilizing the disassembled task-aware components. The entire process is akin to playing with LEGO bricks, enabling arbitrary assembly of new models, and providing a novel perspective for model creation and reuse. Extensive experiments showcase that task-aware components disassembled from CNN classifiers or new models assembled using these components closely match or even surpass the performance of the baseline, demonstrating its promising results for model reuse. Furthermore, MDA exhibits diverse potential applications, with comprehensive experiments exploring model decision route analysis, model compression, knowledge distillation, and more. For more information, please visit https://model-lego.github.io/ .

1 Introduction

Convolutional Neural Networks (CNNs), as the predominant architecture in deep learning, play a crucial role in image, video, and audio processing [1, 2, 3]. CNNs were originally inspired by the concept of receptive fields in the biological visual system [4], and our focus is to explore and leverage similar characteristics within CNNs. Various studies [5, 6, 7] have delved into unraveling the intricacies of biological visual information processing systems. Notably, Livingstone et al. [6] substantiated that the intermediate visual cortex comprises relatively independent subdivisions.

Some studies [8, 9, 10, 11, 12, 13, 14] have endeavored to visualize critical layers and neurons within CNNs. These visualizations demonstrate that in the more shallow layers of CNNs, form, color, texture, and edge features are processed by distinct convolutional kernels, whereas deeper layers are responsible for category-aware features. The above phenomena of feature processing within CNNs demonstrates the similarity to the parallel processing mechanism [5, 6] and the information integration theory [7] proposed in biological visual systems. Consequently, this paper is dedicated to the exploration and practical utilization of these distinct subsystems within CNNs.

In pursuit of this exploration, we introduce a pioneering task named Model Disassembling and Assembling (MDA), a novel approach to construct and combine subsystems with LEGO-like flexibility. The conceptual framework behind MDA posits that, much like assembling and disassembling LEGO structures, deep learning models can undergo such operations without incurring significant training overhead or compromising performance. This task is designed to be universally applicable, spanning various existing Deep Neural Networks (DNNs), such as Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), Transformers, and others.

However, constructing the MDA framework presents several challenges, notably in determining the minimal disassembling unit and devising an assembly process with minimal impact on performance. Existing works either require the predefinition of task units during the initial training stage, or the disassembled unit cannot be directly used for inference or assembly [15, 16, 17]. In contrast, our approach distinguishes itself as the inaugural attempt to directly disassemble a trained network into task-aware components, avoiding the need for additional networks or fine-tuning. This ensures efficiency and interpretability throughout the disassembling and assembling processes.

In this paper, we illustrate our approach using CNN classifiers, with the assurance that its applicability extends to other Deep Neural Network (DNN) architectures. In the disassembling phase, we define the task-aware component by introducing the concept of relative contribution and a mechanism for contribution aggregation and allocation. This is seamlessly applied throughout the forward propagation process of the network. Building on these principles, we introduce a component locating technique that discerns and extracts task-aware components. In the assembling phase, we propose a simple yet effective alignment padding strategy. This involves padding empty kernels onto each convolutional filter to ensure uniform kernel counts across all filters in each layer. Additionally, to account for varying feature magnitudes across different components, we implement a parameter scaling strategy. The resulting MDA framework facilitates recombination among different pre-trained models, providing a vital technique for model reuse.

Our contributions in this study are summarized as follows:

We introduce a novel task, MDA, which aims to disassemble and assemble deep models in an interpretable manner reminiscent of playing with LEGOs. This task is motivated by the subdivision of the biological visual system [6].

We present the inaugural method for MDA, specifically applied to CNN classifiers. In the model disassembling phase, we introduce a component locating technique to disassemble task-aware components from the models. For model assembling, we propose an alignment padding strategy and a parameter scaling strategy to assemble task-aware components into a new model.

Extensive experiments validate the efficacy of our proposed MDA method, showing that the performance of the disassembled and assembled models closely matches or even surpasses that of the baseline models.

MDA introduces a fresh perspective for model reuse. Additionally, we explore diverse applications of MDA, including decision route analysis, model compression, knowledge distillation, and more.

2 Model Disassembling and Assembling

In this section, we present the definition of Model Disassembling and Assembling (MDA). Let us consider a set of N pre-trained deep learning models denoted as {M ( n ) } N n =1 . Each model M ( n ) comprises K ( n ) subtasks, represented as { t ( n ) k } K ( n ) k =1 . Our objective in model disassembling is to extract the model components M [ t ( n ) k ] corresponding to a specific subtask t ( n ) k from the source

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model M ( n ) . These extracted components M [ t ( n ) k ] are intended to encapsulate only the parameters critically relevant to the subtask t ( n ) k . In essence, the disassembled components M [ t ( n ) k ] should function as an independent model, preserving the full capability for the subtask t ( n ) k without redundant capability for other subtasks. Furthermore, the assembling process involves combining disassembled components from different models. For example, combining components M [ t ( n 1 ) k 1 , . . . , t ( n 1 ) k 2 ] from M ( n 1 ) and M [ t ( n 2 ) k 3 , . . . , t ( n 2 ) k 4 ] from M ( n 2 ) results in a new model M ( new ) . This assembled model M ( new ) = M [ t ( n 1 ) k 1 , . . . , t ( n 1 ) k 2 , t ( n 2 ) k 3 , . . . , t ( n 2 ) k 4 ] is expected to retain full functionality for the subtasks { t ( n 1 ) k 1 , . . . , t ( n 1 ) k 2 } and { t ( n 2 ) k 3 , . . . , t ( n 2 ) k 4 } . ' ( (#) " ( (#) ( ( (#$!) )

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3 Model Disassembling

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& ! (#$!) & & (#$!) & ( (#$!) + ) (#) ℂ ) (#) ℂ ! (#) ℂ & (#) ℂ ) (#) ℂ & (#) + & (#) ( & (#) ( & (#$!) , , To illustrate this process more specifically, we focus on the l -th convolutional layer of a CNN (the case of the fully connected layer is discussed in Appendix A). The l -th layer has P input channels and Q output channels. Correspondingly, the l -th layer comprises Q convolution filters, denoted as { C ( l ) q } Q q =1 . Each filter C ( l ) q consists of P convolution kernels, thus C ( l ) q = { c ( l ) q,p } P p =1 . The input feature maps for the l -th layer are represented as { a ( l ) p } P p =1 . With the convolution filter C ( l ) q , the hidden feature maps A ( l ) q for the q -th channel in the l -th layer are computed as follows:

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where b ( l ) q is the bias for the q -th channel. From Eqn.(2), it is evident that the output feature map a ( l +1) q is the summation of the hidden feature maps A ( l ) q . This implies that the value of a ( l +1) q is influenced by the value of each individual hidden feature map a ( l ) q,p . Similar to the Softmax operation in the final layer, the larger the individual hidden feature map, the greater its contribution to the output a ( l +1) q .

3.1.1 Contribution Aggregation

The red solid line in Fig. 1 represents the contribution aggregation process. In this process, the contributions from the input feature maps { a ( l ) p } P p =1 are aggregated to the hidden feature maps A ( l ) q via the convolution filter C ( l ) q of the q -th channel. To quantify the contribution of each hidden feature map a ( l ) q,p to the output a ( l +1) q , we introduce a metric s ( l ) q,p defined as follows:

where H ( l ) and W ( l ) denote the height and width, respectively, of the feature map a ( l ) q,p . The term a ( l ) q,p [ h, w ] represents the pixel value at the h -th row and w -th column of a ( l ) q,p . Considering the presence of activation functions such as ReLU, negative contributions are treated as having zero impact on the result. Consequently, the contribution ˆ s ( l ) q,p of the hidden feature map a ( l ) q,p is recalculated as:

In line with the principle of the Softmax operation, the contribution of each hidden feature map is relative. Hence, we employ min-max normalization to obtain the relative contribution r ( l ) q,p of each hidden feature map a ( l ) q,p :

where ε is a small constant added to prevent division by zero. This normalization process ensures that the contributions are scaled relative to each other, facilitating the identification of the most influential hidden feature maps in the layer.

3.1.2 Contribution Allocation

The black dashed line in Fig. 1 represents the process of contribution allocation, where the contribution from the feature map a ( l ) p is allocated to various hidden feature maps { a ( l ) q,p } Q q =1 through different convolution kernels { c ( l ) q,p } Q q =1 . Consequently, the overall contribution s ( l ) p of the feature map a ( l ) p is calculated using the following equation:

In this calculation, similar to the previous steps, negative contributions are considered as zero:

Moreover, acknowledging that the significance of each feature map is relative, the relative contribution r ( l ) p of the feature map a ( l ) p is computed as follows:

3.2 Component Locating Technique

Building upon the concept of relative contribution and the mechanism of contribution aggregation and allocation, we introduce a novel approach termed the component locating technique. This method is designed to identify task-aware components, that is, the parameters most relevant to a given task, within a CNN. Taking the l -th layer as an example, the initial stage of this technique involves identifying the most relevant feature maps for the targeted task. Subsequently, the next step is to pinpoint the most relevant parameters by discerning which parameters are linked to the identified most relevant feature maps. For a specific task, please refer to Appendix B.

3.2.1 Relevant Feature Identifying

In essence, the most relevant feature maps are those which exhibit a relatively larger contribution to the result of the model. In the process of contribution aggregation, a threshold value denoted as α is employed to discern whether a relative contribution r ( l ) q,p (calculated in Eqn. 5) is large or small:

where α is a chosen value within the range (0,1]. Through the above equation, the soft relative contribution r ( l ) q,p , which are continuous values indicating the degree of contribution, are transformed into hard relative contribution ˆ r ( l ) q,p . The s ( l ) p in Eqn. 6 is now the sum over the hard relative contribution ˆ r ( l ) q,p . Eqn.7 and Eqn.8 remain unchanged.

Further, a second threshold value β is used to determine whether the relative contribution r ( l ) p (as defined in Eqn.8) of a feature map is large or small:

where β is a chosen value within the range (0,1]. This equation transforms the soft relative contribution r ( l ) p into hard relative contribution ˆ r ( l ) p , just as in the case of ˆ r ( l ) q,p .

3.2.2 Parameter Linking

In Eqn. 10, the hard relative contribution ˆ r ( l ) p , being either 0 or 1, indicates whether the feature map a ( l ) p is most relevant to the predicted result. Similarly, the hard relative contribution ˆ r ( l +1) q can also reflect whether the feature map a ( l +1) q is most relevant to the predicted result. Integrating Eqn.1 with Eqn.2, we can represent the convolutional process as follows:

This equation signifies that the output feature map a ( l +1) q is generated by the convolution operation of the input feature maps { a ( l ) p } P p =1 with the respective convolution kernels { c ( l ) q,p } P p =1 , which collectively form the convolution filter C ( l ) q . Thus, if the output feature map a ( l +1) q is determined to be most relevant to the predicted result, then it logically follows that the convolution filter C ( l ) q and the associated bias b ( l ) q are also most relevant to the predicted result. Furthermore, each input feature map a ( l ) p engages in the convolution operation exclusively with the kernels { c ( l ) q,p } Q q =1 across different convolution filters. Therefore, if the feature map a ( l ) p is identified as most relevant to the predicted result, then the convolution kernels { c ( l ) q,p } Q q =1 associated with it are also deemed most relevant to the predicted result. The component locating technique is also applicable to fully connected layers, enabling the identification of the most relevant filters, kernels, and biases.

In summary, through the component locating technique, we can effectively identify and discriminate the most relevant components to a specific task. This includes pinpointing the most relevant filters, kernels, and biases. Subsequently, the identified parameters most relevant to a specific task can be extracted from the source model through a process known as structure pruning [18].

4 Model Assembling

In the process of CNN model assembling, our objective is to combine disassembled, task-aware components, typically derived from different source models, into a new model. This assembling is performed without necessitating retraining or incurring performance loss. To achieve this goal, we propose an alignment padding strategy and a parameter scaling strategy. It is important to note that the focus of this paper is specifically on assembling different models that share isomorphic network architectures.

4.1 Alignment Padding Strategy

(b) In the assembling of CNN models, we combine models layer by layer along the dimension of the filters. A challenge in this process is that filters from different models may have varying numbers of kernels. To address this, we introduce a simple yet effective alignment padding strategy. This strategy involves padding empty kernels to each filter, ensuring that all filters in a given layer have a uniform number of kernels.

Consider the l -th layer as an example. As depicted in Fig.2(a), a disassembled model consists of J convolution filters, denoted as { C ( l ) j } J j =1 . Each convolution filter C ( l ) j comprises I convolutional kernels, expressed as C ( l ) j = { c ( l ) j,i } I i =1 . Another disassembled model, shown in Fig.2(b), contains V convolution filters { C ( l ) v } V v =1 , with each filter C ( l ) v consisting of U convolutional kernels, expressed as C ( l ) v = { c ( l ) v,u } U u =1 . In the assembling process, illustrated in Fig.2(c), these two models are merged into a new model with J + V convolution filters. Each filter in this new model is augmented with a complementary number of empty kernels. Specifically, if a convolution filter ˆ C ( l ) j originates from C ( l ) j , it is padded with U empty kernels at the end, forming ˆ C ( l ) j = { c ( l ) j, 1 , c ( l ) j, 2 , . . . , c ( l ) j, I , 0 1 , 0 2 , . . . , 0 U } . Conversely, if a convolution filter ˆ C ( l ) v comes from C ( l ) v , it is padded with I empty kernels at the beginning, resulting in ˆ C ( l ) v = { 0 1 , 0 2 , . . . , 0 I , c ( l ) v, 1 , c ( l ) v, 2 , . . . , c ( l ) v, U } . Through this alignment padding strategy, every convolution filter in the assembled model is standardized to have the same total of I + U convolution kernels.

The alignment padding strategy can be readily extended to incorporate multiple disassembled models and applied across all layers of a CNN. A key feature of this approach is that the assembled model is ready for inference immediately, without the need for any retraining.

4.2 Parameter Scaling Strategy

In addition to the alignment padding strategy, we address another critical issue in model assembling: the potential disparity in the magnitude of features output by disassembled components from different source models. If left unaddressed, this disparity could cause the assembled model to be biased towards the disassembled components with larger feature magnitudes, impacting the balance and effectiveness of the assembled model. To resolve this, we propose a parameter scaling strategy.

Taking the l -th layer as an example, as shown in Fig.2(a) and (b), let's consider the output feature maps in the l -th layer of the disassembled models, denoted as { a ( l +1) j } J j =1 and { a ( l +1) v } V v =1 , respectively. The magnitude of each feature map a ( l +1) is quantified as follows:

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Similarly, the magnitude e ( l +1) v for feature map a ( l +1) v is calculated using the same equation. We then compute the average magnitude of the feature maps from all disassembled models in the l -th layer:

Then, the convolution filter C ( l ) j is scaled as follows:

In practical applications, this parameter scaling strategy is particularly crucial in the last fully connected layer to ensure that the magnitude differences in the final outputs of disassembled models from different source models are not excessively large.

5 Experiments

5.1 Experimental Settings

Dataset and Network. We select three datasets and three mainstream CNN classifiers to evaluate our MDA method. The datasets include CIFAR-10 [19], CIFAR-100 [19], and Tiny-ImageNet [20]. The chosen CNN classifiers are VGG-16 [21], ResNet-50 [22], and GoogleNet [23].

Parameter Configuration. In our MDA method, the key parameters are α and β , as defined in Eqn.9 and Eqn.10, respectively. By default, we set α = 0 . 3 and β = 0 . 2 in convolutional layers, and α = 0 . 4 and β = 0 . 3 in fully connected layers, unless specified otherwise. The model training is conducted using the SGD optimizer, with a learning rate of 0 . 01 . To ensure the reliability and reproducibility of our results, we report the average of three independent experimental runs for each result. Comprehensive details and the source code can be found in the Supplementary Material .

5.2 MDAApplied to CNN Models

5.2.1 Model Disassembling Results

We present the results of model disassembling in Table 1. The results in Table 1 reveal that both single-task and multi-task disassembling, as executed by our method (denoted as 'Disa.'), exhibit accuracies comparable to, or even surpassing, those of the source model (denoted as 'Base.'). Notably, disassembling single tasks '0' or '1' from CIFAR-10 when using GoogleNet achieved a 100% accuracy rate. Similarly, the accuracy for disassembling multiple tasks '70-169' from Tiny-ImageNet on ResNet-50 showed an improvement of over 2.15% compared to the source model. What's more, to

go deeper into the performance of our proposed disassembled method, we present the disassembling results on ImageNet [24] and the comparison of parameter size and Floating Point Operations Per Second (FLOPs) in the Supplementary Material .

5.2.2 Model Assembling Results

The performance of model assembling across different datasets is presented in Table 2. It is observed that the assembled models generally achieve comparable performance to the source models in both single-task and multi-task assembling settings. Notably, the assembled models combining '0-2 + 0-69' from 'CIFAR-10 + Tiny-ImageNet' on GoogleNet surpass the source model in terms of accuracy. However, there are instances, such as with '20-69 + 70-179' from 'CIFAR-100 + Tiny-ImageNet' on ResNet-50, where a decrease in accuracy is noted. This could be attributed to the interaction and interference among the numerous parameters from the different models being assembled, particularly when the number of tasks is large, leading to less stable predictions in the new model.

5.3 MDAApplied to GCN Model

The proposed MDA method extends beyond CNN models and is equally applicable to Graph Convolutional Network (GCN) models [25]. We demonstrate this by conducting a disassembling experiment for node classification using a GCN model [25] on the Cora dataset [26]. The results of this experiment are presented in Table 3.

The results clearly indicate that the accuracy of the disassembled GCN model surpasses that of the source model. For instance, the accuracy of the disassembled model for categories '1-2' shows an improvement of 1 . 47% over the source GCN model.

5.4 Ablation Study

Parameters α and β . Fig. 3 presents an ablation study on the thresholds α and β in the fully connected layer and convolutional layer. With the increase of the thresholds α and β , fewer parameters will be regarded as relevant to the specific tasks. Therefore, in Fig. 3(a), we observe that as the

Table 3: Performance of the GCN model disassembling on the Cora Dataset. 'Base.' represents the average accuracy for the 'Disassembled Task' in the source model. In 'Disa.', 'Score1 ( +Score2 )' indicates the accuracy and the improvement in accuracy (' Score2 ') compared to 'Base.' for the 'Disassembled Task' of the disassembled model.

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Number of Disassembled Layers. Fig. 4 shows the ablation study on the number of disassembled layers. As depicted in Fig. 4(a), the accuracy initially increases and then decreases with the rising number of disassembled layers, but consistently remains higher than the source model. Additionally, Fig. 4(b, c) reveals that both the FLOPs ratio and parameter size ratio decline as more layers are disassembled. This ablation study validates that our proposed disassembled method can effectively disassemble all layers of the model.

Assembling Strategy. The impact of different assembling strategies is detailed in Table 4. It is observed that the method combining padding alignment and parameter scaling ('+Padd. +Para.') results in higher accuracy compared to the strategy employing only padding alignment ('+Padd.'). In the case of '0 + 0', the '+Padd. +Para.' approach leads to a significant accuracy increase of 37.00%. These results affirm the effectiveness of the assembling strategies proposed in this paper.

6 Related Works

6.1 Model Explanation

Model explanation methods can generally be categorized into four types: activation-based, gradientbased, perturbation-based techniques, and Layer-wise Relevance Propagation (LRP). Activation-based approaches [27, 28, 29, 30, 31, 32, 33, 34] involve calculating a set of weights and then aggregation feature maps to highlight crucial features. Gradient-based methods [35, 36, 37, 38, 39, 40, 21, 41, 42] utilize gradients to identify key features. Perturbation-based techniques [43, 44, 45, 46] discern important features by altering or masking them and observing the resultant changes in output. LRP involves backward propagation of the final prediction through the network using specific local propagation rules, grounded in the principle of conservation [47]. Montavon et al. [48] provided a comprehensive review of LRP rules, including LRP-0, LRPϵ , LRPγ , LRPαβ , flat, ω 2 -rules, and z B -rule, and discussed their distinctions and interconnections. Additionally, Ancona et al. [49] examined various gradient-based techniques (such as Gradient × Input, Integrated Gradients, and DeepLIFT) and LRP from both theoretical and practical angles, highlighting their similarities and

Table 4: Comparison of assembling strategies. 'Base.' represents the average accuracy for the 'Assembled Task' in the source models. '+Padd.' and '+Padd. +Para.' denote the accuracy of the assembled classifier using only the padding alignment strategy and both the padding alignment and parameter scaling strategies, respectively.

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conditions for equivalence or approximation. While these model explanation techniques are primarily employed to locate important features in the original input contributing to the final prediction, our focus is distinct. We aim to identify task-aware components, namely the relevant parameters for specific tasks, for model disassembling.

6.2 Subnetwork Identification

Subnetwork identification approaches can be divided into ante-hoc and post-hoc techniques. Antehoc techniques, such as those proposed by Li et al. [50] and Liang et al. [15], incorporate novel architectural control modules to select specific filters or employ category-specific gating during training, mainly for network interpretation and adversarial sample detection. Post-hoc techniques are further subdivided. The first family requires additional learnable modules and extra training steps. For instance, Hu et al. [16] introduced Neural Architecture Disentanglement (NAD) for disentangling pre-trained DNNs into task-specific sub-architectures, while Wang et al. [51] and Frankle et al. [52] focused on data routing paths and network acceleration, respectively. Furthermore, Yu et al. [53] and Yang et al. [17] used knowledge distillation to dissect and reassemble models. The second family aligns more closely with feature attribution, with techniques such as those of Khakzar et al. [54] employing concepts similar to perturbation-based methods for pathway selection. In summary, while existing subnetwork identification techniques are commonly applied for network interpretation and adversarial sample detection, our work centers on MDA. We focus on disassembling task-aware components from trained CNN classifiers and reassembling them into a new model, akin to playing with LEGOs, without requiring additional training.

7 Limitation and Future Work

Our experiments, as detailed in Table 1 of the main text, demonstrate that models disassembled using the proposed method can surpass the source model in terms of accuracy. However, the performance of the assembled models, as shown in Table 2 of the main text, indicates a decrease in accuracy in certain cases (e.g., '0-19 + 0-69', '20-69 + 70-179', '0-99 + 0-199' for 'CIFAR-100 + Tiny-ImageNet'). This decline in performance could be attributed to the interference of irrelevant components, which may adversely affect the correct prediction of samples. Looking ahead, our research will concentrate on addressing the disturbance caused by irrelevant components and enhancing the effectiveness of our model disassembling and assembling technique, particularly for targeted tasks. Additionally, while this paper has focused exclusively on CNN classifiers, future research will explore the disassembling and assembling of models in other domains, including object detection and segmentation.

8 Conclusion

In this paper, we introduce a novel Model Disassembling and Assembling (MDA) task, inspired by the subdivision of the visual system [6], with the objective of disassembling and assembling deep models in a manner akin to playing with LEGOs. The primary focus of this paper centers on the application of MDA to CNN classifiers. During model disassembling, we introduce the concept of relative contribution and propose a component locating technique to extract task-aware components from trained CNN classifiers. For model assembling, we introduce the alignment padding strategy and parameter scaling strategy to construct a new model tailored for a specific task using the disassembled task-aware components. Extensive experiments conducted in this study reveal that the performance of the disassembled and assembled models closely aligns with or even surpasses that of the baseline models. In addition to offering a fresh perspective for model reuse, our research extends to the diverse applications of MDA, including decision route analysis, model compression, knowledge distillation, and more. In future work, we will focus on the MDA applied to other models, such as multi-modal models, large language models etc.

Acknowledgments and Disclosure of Funding

This work is funded by National Key Research and Development Project (Grant No: 2022YFB2703100), Ningbo Natural Science Foundation (2022J182) and the Fundamental Research Funds for the Central Universities (226-2024-00145).

References

Zhi-Xuan Chen, Cheng Jin, Tian-Jing Zhang, Xiao Wu, and Liang-Jian Deng. Spanconv: A new convolution via spanning kernel space for lightweight pansharpening. In Proc. 31st Int. Joint Conf. Artif. Intell , pages 1-7, 2022.

Bowei Du, Yecheng Huang, Jiaxin Chen, and Di Huang. Adaptive sparse convolutional networks with global context enhancement for faster object detection on drone images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 13435-13444, 2023.

Xuan Shen, Yaohua Wang, Ming Lin, Yilun Huang, Hao Tang, Xiuyu Sun, and Yanzhi Wang. Deepmad: Mathematical architecture design for deep convolutional neural network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 6163-6173, 2023.

David H Hubel and Torsten N Wiesel. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. The Journal of physiology , 160(1):106, 1962.

D. V. Essen and E. A. Deyoe. Concurrent processing in the primate visual cortex. Cognitive Neuroence , pages 383-400, 1995.

M. S. Livingstone and D. H. Hubel. Psychophysical evidence for separate channels for the perception of form, color, movement, and depth. Journal of Neuroscience , 7(11):3416-3468, 1987.

Treisman and M. Anne. Selective attention in man. Br Med Bull , 20(1):12-16, 1963.

D. Erhan, Y. Bengio, A. Courville, and P. Vincent. Visualizing higher-layer features of a deep network. University of Montreal , 2009.

A. Mahendran and A. Vedaldi. Understanding deep image representations by inverting them. CVPR , 2015.

A. Nguyen, J. Yosinski, and J. Clune. Multifaceted feature visualization: Uncovering the different types of features learned by each neuron in deep neural networks. CVPR , 2016.

Christoph Feichtenhofer, Axel Pinz, Richard P Wildes, and Andrew Zisserman. Deep insights into convolutional networks for video recognition. International Journal of Computer Vision , 128:420-437, 2020.

Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, and Jeff Clune. Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. NeurIPS , 2016.

K. Simonyan, A. Vedaldi, and A. Zisserman. Deep inside convolutional networks: Visualising image classification models and saliency maps. Computer Science , 2013.

B. Sterling. Inceptionism: going deeper into neural networks. Google Research Blog , 2011.

Haoyu Liang, Zhihao Ouyang, Yuyuan Zeng, Hang Su, Zihao He, Shu-Tao Xia, Jun Zhu, and Bo Zhang. Training interpretable convolutional neural networks by differentiating class-specific filters. In ECCV , pages 622-638. Springer, 2020.

Jie Hu, Liujuan Cao, Tong Tong, Qixiang Ye, Shengchuan Zhang, Ke Li, Feiyue Huang, Ling Shao, and Rongrong Ji. Architecture disentanglement for deep neural networks. In ICCV , pages 672-681, 2021.

Xingyi Yang, Zhou Daquan, Songhua Liu, Jingwen Ye, and Xinchao Wang. Deep model reassembly. arXiv preprint arXiv:2210.17409 , 2022.

Gongfan Fang, Xinyin Ma, Mingli Song, Michael Bi Mi, and Xinchao Wang. Depgraph: Towards any structural pruning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 16091-16101, 2023.

Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009.

Ya Le and Xuan Yang. Tiny imagenet visual recognition challenge. CS 231N , 7(7):3, 2015.

Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. Deep inside convolutional networks: Visualising image classification models and saliency maps. In ICLR Workshop , 2013.

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR , pages 770-778, 2016.

Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In CVPR , pages 1-9, 2015.

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR , pages 248-255. Ieee, 2009.

Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 , 2016.

Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. Collective classification in network data. AI magazine , 29(3):93-93, 2008.

Saurabh Desai and Harish G. Ramaswamy. Ablation-cam: Visual explanations for deep convolutional network via gradient-free localization. In WACV , pages 983-991, 2020.

Rakshit Naidu and Joy Michael. Ss-cam: Smoothed score-cam for sharper visual feature localization. arXiv preprint arXiv:2006.14255 , 2020.

Aravindh Mahendran and Andrea Vedaldi. Visualizing deep convolutional neural networks using natural pre-images. International Journal of Computer Vision , 120:233-255, 2016.

Haofan Wang, Zifan Wang, Mengnan Du, Fan Yang, Zijian Zhang, Sirui Ding, Piotr Mardziel, and Xia Hu. Score-cam: Score-weighted visual explanations for convolutional neural networks. In CVPR Workshops , pages 111-119, 2020.

Shunyu Liu, Jie Song, Yihe Zhou, Na Yu, Kaixuan Chen, Zunlei Feng, and Mingli Song. Interaction pattern disentangling for multi-agent reinforcement learning. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024.

Jianming Zhang, Sarah Adel Bargal, Zhe Lin, Jonathan Brandt, Xiaohui Shen, and Stan Sclaroff. Top-down neural attention by excitation backprop. International Journal of Computer Vision , 126(10):1084-1102, 2018.

Lixiang Ru, Bo Du, Yibing Zhan, and Chen Wu. Weakly-supervised semantic segmentation with visual words learning and hybrid pooling. International Journal of Computer Vision , 130(4):1127-1144, 2022.

B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba. Learning deep features for discriminative localization. IEEE Computer Society , 2016.

Aditya Chattopadhay, Anirban Sarkar, Prantik Howlader, and Vineeth N Balasubramanian. Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks. In WACV , pages 839-847, 2018.

Z. Feng, J. Hu, S. Wu, X. Yu, J. Song, and M. Song. Model doctor: A simple gradient aggregation strategy for diagnosing and treating cnn classifiers. AAAI , 2022.

Daniel Omeiza, Skyler Speakman, Celia Cintas, and Komminist Weldemariam. Smooth grad-cam++: An enhanced inference level visualization technique for deep convolutional neural network models. arXiv preprint arXiv:1908.01224 , 2019.

Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. International Journal of Computer Vision , 128(2):336-359, 2020.

Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. Learning important features through propagating activation differences. In ICML , 2017.

Avanti Shrikumar, Peyton Greenside, Anna Shcherbina, and Anshul Kundaje. Not just a black box: Learning important features through propagating activation differences. arXiv preprint arXiv:1605.01713 , 2016.

Jie Song, Yixin Chen, Xinchao Wang, Chengchao Shen, and Mingli Song. Deep model transferability from attribution maps. In NeurIPS , volume 32, pages 6179-6189, 2019.

Mukund Sundararajan, Ankur Taly, and Qiqi Yan. Axiomatic attribution for deep networks. In ICML , 2017.

Benjamin J. Lengerich, Sandeep Konam, Eric P. Xing, Stephanie Rosenthal, and Manuela M. Veloso. Visual explanations for convolutional neural networks via input resampling. arXiv preprint arXiv:1707.09641 , 2017.

Matthew D. Zeiler and Rob Fergus. Visualizing and understanding convolutional networks. In ECCV , 2014.

Jian Zhou and Olga G Troyanskaya. Predicting effects of noncoding variants with deep learning-based sequence model. Nature Methods , 12(10):931-934, 2015.

Luisa M Zintgraf, Taco S Cohen, Tameem Adel, and Max Welling. Visualizing deep neural network decisions: Prediction difference analysis. In ICLR , 2017.

S. Bach, A. Binder, G. Montavon, F. Klauschen, KR Müller, and W. Samek. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLOS ONE , 10, 2015.

Grégoire Montavon, Alexander Binder, Sebastian Lapuschkin, Wojciech Samek, and Klaus-Robert Müller. Layer-wise relevance propagation: an overview. Explainable AI: interpreting, explaining and visualizing deep learning , pages 193-209, 2019.

Marco Ancona, Enea Ceolini, Cengiz ztireli, and Markus Gross. Towards better understanding of gradientbased attribution methods for deep neural networks. In ICLR , 2018.

Yuchao Li, Rongrong Ji, Shaohui Lin, Baochang Zhang, Chenqian Yan, Yongjian Wu, Feiyue Huang, and Ling Shao. Interpretable neural network decoupling. In ECCV , pages 653-669. Springer, 2020.

Yulong Wang, Hang Su, Bo Zhang, and Xiaolin Hu. Interpret neural networks by identifying critical data routing paths. In CVPR , pages 8906-8914, 2018.

Jonathan Frankle and Michael Carbin. The lottery ticket hypothesis: Finding sparse, trainable neural networks. arXiv preprint arXiv:1803.03635 , 2018.

Sihao Yu, Fei Sun, Jiafeng Guo, Ruqing Zhang, and Xueqi Cheng. Legonet: A fast and exact unlearning architecture. arXiv preprint arXiv:2210.16023 , 2022.

Ashkan Khakzar, Soroosh Baselizadeh, Saurabh Khanduja, Christian Rupprecht, Seong Tae Kim, and Nassir Navab. Neural response interpretation through the lens of critical pathways. In CVPR , pages 13528-13538, 2021.

Yann Lecun, Leon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. ISP , pages 306-351, 2001.

Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 , 2014.

Yang He, Ping Liu, Ziwei Wang, Zhilan Hu, and Yi Yang. Filter pruning via geometric median for deep convolutional neural networks acceleration. In CVPR , pages 4340-4349, 2019.

Mingbao Lin, Rongrong Ji, Yan Wang, Yichen Zhang, Baochang Zhang, Yonghong Tian, and Ling Shao. Hrank: Filter pruning using high-rank feature map. In CVPR , pages 1529-1538, 2020.

Chengchao Shen, Xinchao Wang, Jie Song, Li Sun, and Mingli Song. Amalgamating knowledge towards comprehensive classification. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 33, pages 3068-3075, 2019.

Han Xiao, Kashif Rasul, and Roland Vollgraf. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 , 2017.

A Contribution Aggregation and Allocation in the Fully Connected Layer

Figure 5 illustrates the scenario of contribution aggregation and allocation in the fully connected (FC) layer within Convolutional Neural Networks (CNNs). The FC layer is composed of Q filters { C ( l ) q } Q q =1 , each comprising P kernels C ( l ) q = { c ( l ) q,p } P p =1 . Notably, in the FC layer, both kernels and features are single real numbers, i.e., a ( l ) p ∈ R and c ( l ) q,p ∈ R , contrasting with the convolutional layer where kernels and features are two-dimensional matrices of real numbers, denoted as a ( l ) p ∈ R H × W and c ( l ) q,p ∈ R H ( l ) k × W ( l ) k (with H ( l ) and W ( l ) representing the height and width of features, and H ( l ) k and W ( l ) k representing the height and width of kernels).

Given this structural discrepancy, the method for computing contributions in fully connected layers requires adaptation. Specifically, the contribution s ( l ) q,p defined in Eqn.(3) can be directly equated to the hidden feature a ( l ) q,p as expressed below:

The remaining equations in the main text remain unchanged.

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B Component Locating Technique for a Specific Task

Figure 6 illustrates a visualization of the soft relative contribution r ( l ) p for input feature maps in an intermediate convolutional layer of a model. Additional visualizations of relative contribution are available in §I. Notably, in Fig. 6(a), an intriguing observation emerges: different input samples belonging to the same category display similar patterns in terms of soft relative contribution. This consistency suggests that channels making significant contributions to the classification of a particular category tend to exhibit consistency across diverse samples within that category. Essentially, specific categories are closely associated with fixed convolution filters and kernels.

Conversely, as depicted in Fig. 6(b), input samples from distinct categories manifest distinct patterns of soft relative contribution. This variability implies that channels contributing substantially to classification vary across different categories. In essence, each category is linked to a unique set of convolution filters and kernels.

Therefore, considering the l -th layer as an example, to identify components associated with a specific task (category), we initially select 1% of samples accurately classified for this category with the highest predicted probability. Subsequently, we average the features of these selected samples and calculate the relative contribution following the description in the main body of the paper.

C Relevant Feature Identifying with Backward Consideration

The computation of the relative contribution ˆ r ( l ) p for the input feature map a ( l ) p in the l -th layer employs a backward approach. Consequently, it is imperative to account for the relative contribution of the input feature maps in the subsequent ( l +1) -th layer when computing the relative contribution in the l -th layer. Specifically,

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the input feature maps in the ( l +1) -th layer correspond to the output feature maps { a ( l +1) q } Q q =1 of the l -th layer. Therefore, the relative contribution { ˆ r ( l +1) q } Q q =1 of these output feature maps in the l -th layer is equivalent to the relative contribution of the input feature maps in the ( l +1) -th layer.

In accordance with Eqn.(2), if the relative contribution ˆ r ( l +1) q for the output feature map a ( l +1) q is zero, then the relative contribution { r ( l ) q,p } P p =1 of the hidden feature maps { a ( l ) q,p } P p =1 is also considered as zero. Consequently, in Eqn.(9), the hard relative contribution ˆ r ( l ) q,p for the hidden feature map a ( l ) q,p is recalculated as follows:

The remaining equations in the main text remain unchanged.

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D Alignment Padding Strategy for Inception Module

Due to the distinctive multi-branch concatenation architecture, specifically the inception module, the default alignment padding strategy encounters challenges when applied to GoogleNet [23]. To address this, we have enhanced the alignment padding strategy specifically tailored for the inception module.

As illustrated in Fig. 7(a), the conventional alignment padding is employed in each convolutional filter by default, resulting in blank features calculated by zero kernels being dispersed throughout the channel at various locations after the concatenate operation. In our improved alignment padding strategy, depicted in Fig. 7(b), the alignment padding is applied after the concatenate operation.

E More Experiments of CNN Model Disassembling

To further investigate the efficacy of the proposed disassembling method, we present the parameter size and Floating Point Operations Per Second (FLOPs) in Table 5. It is evident that both the parameter size (referred to as 'Para.') and FLOPs increase with the number of disassembled categories. For instance, the parameter size of the model disassembled for categories '3-9' from MNIST using VGG-16 is higher than that of the model disassembled for categories '0-2' from the same dataset and network.

Furthermore, our observations reveal that as the source model encompasses more categories, the individual components associated with each category tend to be smaller. For example, both the FLOPs and 'Para.' for the model disassembled component for category '0' from Tiny-ImageNet on VGG-16 are less than those for the same category '0' from MNIST on the same network. This suggests that the complexity and resource requirements of disassembled models are influenced not only by the number of categories they comprise but also by the inherent diversity and complexity of the datasets from which they are derived.

F More Experiments of CNN Model Assembling

Table 6 provides detailed insights into the comparative performance of models with and without assembling. The results indicate that models with assembling generally exhibit reduced performance compared to those without assembling. For instance, the accuracy of '0-2 + 0-19' from CIFAR-10 + CIFAR-100 on VGG16 decreases after assembling. This decline in accuracy may be attributed to the possibility that samples from a specific category could inadvertently attain high confidence in other unrelated components of the assembled models. A notable example involves similar features across different categories, such as a monkey (from model M (1) ) and a gorilla

(from model M (2) ). In such cases, a sample from one category might receive a higher confidence score from the model trained on the other category.

However, a key advantage of model assembling lies in the reduction of inference time. As the number of disassembled components increases, running each component independently becomes more time-consuming compared to utilizing an assembled model. This underscores the trade-off between model performance and computational efficiency in the assembling process.

G More Experiments of CNN Model on Large-scale Datasets

It is crucial to investigate the scalability of the proposed framework on large-scale datasets, such as ImageNet [24]. Table 7 presents the disassembling performance on ImageNet with VGG-16, demonstrating that the performance of the disassembled model surpasses that of the source model in all cases. Specifically, when disassembling categories '0-99', the accuracy of the disassembled model is higher by 3.18% compared to the source model. These experiments underscore the scalability of the proposed method, which extends its applicability to mainstream benchmark datasets.

H MDAApplied to Other Domains

The proposed MDA method demonstrates significant flexibility and utility beyond its primary application. Specifically, it enables the customization and reuse of pre-trained CNN classifiers for specific tasks without

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necessitating additional training. This adaptability extends to various other tasks, including but not limited to model decision route analysis, model compression, knowledge distillation.

H.1 Model Decision Route Visualization

The concept of a 'decision route' refers to the specific data flow pathways utilized for each category within a deep learning model. These pathways are typically sparse and remain fixed. As elucidated in §B, the prediction for different categories within a model hinges on the large contributions from distinct feature maps, meaning each category is associated with its own set of relevant model parameters. The proposed framework for contribution allocating and aggregating, combined with component locating techniques, facilitates the construction of unique decision routes for each category.

Such decision route analysis offers several benefits. Firstly, it enhances the explainability of deep models. By comparing the decision route of a misclassified sample with the corresponding correct category's route, researchers can identify where the data flow diverged incorrectly. Additionally, these visualizations serve as powerful tools for a deeper understanding and exploration of deep classifiers.

For example, Fig.8 illustrates decision routes for specific categories using LeNet-5 [55], which includes two convolutional layers and three linear layers. It is observable that each category possesses distinct decision routes, yet there is some overlap among them. This overlap can be attributed to the fact that shallower convolutional filters often process fundamental features like color, texture, and edges-aligning with existing research [8, 9, 10, 12, 13, 14] and the mechanisms of biological visual information processing [5, 6, 7]. In the deeper layers, particularly the final linear layer, we notice that connections between neurons are sparse yet fixed, reflecting the emergence of category-specific features at deeper levels of the CNN.

In summary, visualizing the decision routes for specific classes within a CNN classifier not only aids in dissecting the classifier's underlying mechanisms but also proves instrumental in debugging and enhancing the model's performance.

H.2 Model Compression

The proposed MDA presents a novel approach to model compression for CNN classifiers. Utilizing our component locating technique, we effectively disassemble category-specific parameters from the source model. This process entails separating parameters utilized across all categories and discarding those that are redundant, i.e., not used by any category. Consequently, this method offers a unique avenue for model compression.

For empirical validation, we conducted compression experiments using three mainstream classifiers: VGG16 [56], ResNet-50 [22], and GoogleNet [23] on the CIFAR-10 dataset [19], as detailed in Table 8. Additionally, we benchmarked our method against state-of-the-art (SOTA) model compression techniques, specifically two pruning-based methods: FPGM [57] and HRank [58].

The results, as shown in Table 8, indicate that our method achieves comparable accuracy to the SOTA methods, FPGM and HRank. However, it is observed that our method exhibits higher Floating Point Operations Per Second (FLOPs) and parameter size compared to these pruning-based methods. This distinction likely stems from our method's focus on disassembling parameters relevant to each category, as opposed to pruning methods which aim to filter out parameters irrelevant to all categories. Consequently, while our method retains parameters commonly used across categories, pruning methods like FPGM may discard some of these components without significantly affecting the final prediction, leading to fewer FLOPs and a lower parameter size.

In future research, we aim to delve deeper into the capabilities and potential of our disassembling and assembling approach in the realm of model compression, exploring ways to enhance its efficiency and effectiveness in reducing model complexity while preserving or even enhancing performance.

H.3 Knowledge Distillation

The proposed MDA focuses on assembling task-aware components disassembled from different models into a new, unified model. This process bears resemblance to Knowledge Amalgamating (KA) as described in Shen et al. [59], where knowledge from multiple 'teacher' models is distilled into a single 'student' model. While the overarching goals of MDA and KA are similar, the techniques employed in each approach differ significantly.

To assess the efficacy of our MDA method in the context of knowledge distillation, we conducted comparative experiments against KA using the VGG-16 [21] model on five benchmark datasets: MNIST [55], FashionMNIST [60], CIFAR-10 [19], CIFAR-100 [19], and Tiny-ImageNet [20]. The results of these experiments are summarized in Table 9.

The results indicate that the proposed MDA method generally achieves higher accuracy than KA, particularly in scenarios with a smaller number of assembled categories, such as the combination of 'MNIST + FASHIONMNIST'. Conversely, as the number of assembled categories increases-for instance, in the case of 'CIFAR-100 + Tiny-ImageNet', the performance of our method tends to decline, even falling below that of the source model and KA. This decrease in accuracy could be attributed to the increased complexity and potential interference when assembling a larger number of task-aware model components. Specifically, for a test image from an unknown category, the flow through all decision routes in the assembled model can lead to confusion and incorrect predictions, particularly if the components are primarily tailored for categories in a different dataset (e.g., dataset 'A'), thus adversely affecting the accuracy of predictions for samples in dataset 'B'.

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I More Visualization of Relative Contribution (§B)

In this section, Figs. 9-12 show additional visualization results of the relative contribution. As depicted in Figs. 9-11, for a specific layer of the networks on different datasets, different categories exhibit distinct patterns of the soft relative contribution. From Fig. 12, it is evident that in different layers of VGG-16, different categories demonstrate varied patterns of the soft relative contribution. These visualizations offer additional insights into the associations between categories and specific filters across various layers of the neural networks. In certain instances, such as in the case of Layer 52 of ResNet50 on CIFAR-10, distinct categories exhibit comparable substantial relative contributions across diverse channels. The potential explanation for this phenomenon lies in the insufficient formation of category-related features within this layer, attributable to the limited number of categories in CIFAR-10 (only ten), coupled with the residual structure and depth of the network. Nonetheless, other layers manifest disparate substantial relative contributions for distinct categories across diverse channels. This phenomenon stands as a pivotal factor in ensuring the accurate prediction performance of ResNet50 on the CIFAR-10 dataset.

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Peer reviews:
comments: SUMMARY:
This paper proposes a novel framework for model disassembling and assembling. A component locating technique is introduced to disassemble task-aware components from the models. And an alignment padding strategy and a parameter scaling strategy are also designed to assemble these useful components.

This work is meaningful to the interpretability of deep neural network models, as it combines the theory of biology and brain science theory to design human-interpretable models, which can uncover the black box CNN.

STRENGTHS:
The paper is clearly written and easy to follow.
The proposed task is very novel, and the whole pipeline is well presented and easy to understand, and makes sense.
The locating-then-assembling paradigm is well supported by the informative components identification and alignment padding strategy.
The authors tested their proposed method on various popular CNN models and obtained convincing results.

WEAKNESSES:
In section 3.1.1, the insight and rationale of the proposed metric is not clear. Why can this metric measure the contribution of features?

Also, in the 4.2, why the Parameter Scaling Strategy can balance the effects of different componens?

QUESTIONS:
refer to the weakness part.

LIMITATIONS:
This paper only design CNN-based method, ignoring the popular Vit model.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 4
DATE: 2024-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper draws inspiration from the information subsystem pathways in biological vision systems and proposes a Model Disassembling and Assembling (MDA) approach.\n\n- For model disassembling, the authors introduce the concepts of contribution aggregation and contribution allocation within convolutional filters and their kernels. The relative contribution of features is calculated and linked to the corresponding parameters for subsequent structure pruning.\n\n- For model assembling, a new model is established by combining the disassembled, task-aware components derived from different source models without necessitating retraining.\n\nTo validate the effectiveness of MDA, the authors conduct extensive experimental evaluations across diverse architectures, including CNNs and GCNs. The results show that MDA can work effectively on these architectures. In addition to model creation, the authors also explore potential applications of MDA, such as model decision route analysis and model compression.\n\nSTRENGTHS:\n1. The concept of Model Disassembling and Assembling (MDA) is novel, offering a new perspective on model architecture. It suggests that each part of a model related to specific tasks or categories can be considered an independent functional component. These components can be flexibly reused and assembled to achieve various combinations for multi-classification tasks.\n\n2. The concept of MDA, along with the proposed methods and formula definitions, is clearly presented. The experiments are well designed, and the comprehensive results validate that MDA can work effectively for model reuse or creation, and other task scenarios. Moreover, the extensive experiments provide deeper insights into MDA, enhancing the overall quality of the paper.\n\n3. The paper is written in a clear and accessible manner. The illustrations, methods, and formulas are easy to understand, and the language used is straightforward. The provided source codes are also well-structured and easy to read.\n\n4. The proposed method demonstrates significant performance benefits. Besides creating models by reusing model components, the basic idea of MDA has the potential to inspire future research directions, such as model interpretability and model architecture design.\n\nWEAKNESSES:\n1. As the number of categories increases, the performance of disassembling decreases. This means that to achieve the best disassembling effect, it is necessary to decompose each category separately, which is time-consuming and may result in a heavier assembled model.\n\n2. Manually setting thresholds (Eqns. 9 and 10) to obtain relative contributions may not guarantee consistently good results across tasks.\n\n3. While the authors claim that model assembling does not necessitate retraining or incur performance loss (Line 204), the assembled models are often inferior to the baseline without retraining or fine-tuning (see Table 1).\n\n4. In the assembled model, different categories fail to follow their own pathways extracted from the model disassembling. For example, a disassembled conv-filter of CIFAR-10-cat0 might generate a high response on CIFAR-100-cat0 because the conv-filter has never seen CIFAR-100-cat0, which can be regarded as inter-class interference. This is why the assembled model initially shows degraded performance. Ideally, model assembling should ensure that pathways between different categories are mutually exclusive.\n\nQUESTIONS:\n1. The results in Table 3 have shown the effectiveness of MDA when applied to GCN models. However, could the authors provide more details on the design of MDA for GCN models?\n\n2. Does model disassembling require additional training? If so, please present the details on how the numerical results in Tables 1 and 3 were obtained.\n\n3. The fine-tuning of the assembled model will inevitably cause a shift in the internal pattern of the model. Do the pathways of the fine-tuned model remain the same as the original ones?\n\n4. Have the authors considered the interference between categories when assembling the disassembled components? Are there any potential solutions to this issue?\n\nLIMITATIONS:\nThe limitations have been discussed in the paper. Additionally, there is no societal impact from the work performed.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2024-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes the Model Disassembling and Assembling (MDA) task for CNN classifiers, introducing techniques for extracting and reassembling task-aware components. Experiments show reassembled models perform comparably or better than original models. The approach offers new applications in decision route analysis, model compression, and knowledge distillation, with future extensions planned for Transformers and multi-modal models.\n\nSTRENGTHS:\n- The idea presented by this paper is novel and the technique adopted for solving the problem is new.\n\nWEAKNESSES:\n1. The definition of sub-task is strongly tied to category, severely limiting it to classification tasks. Additionally, the entire method seems complicated, appearing to be overfitted to classification tasks and the CNN architecture. Its inability to handle other types of tasks, especially self-supervised learning, which is widely recognized as the future, raises concerns about the value of this work.\n2. There is a lack of systematic comparisons with other works, such as deep model reassembly[1], and the study only includes self-constructed specific tasks, making it hard to evaluate the technical soundness.\n3. Given the widespread adoption of transformers in the field, the absence of experiments involving transformers is unfortunate.\n\n[1] Yang, Xingyi, et al. \"Deep model reassembly.\" Advances in neural information processing systems 35 (2022): 25739-25753.\n\nQUESTIONS:\nCould you provide an intuitive explanation for why your method sometimes outperforms the baseline, given that the baseline is end-to-end trained for the classification task?\n\nLIMITATIONS:\nAs stated in the weakness part, the absence of the results on widely-adopted transformer architectures and its inapplicability to models trained with tasks other than classification (e.g., masked image modeling) may limit the value of this work.", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-08"}

comments: SUMMARY:
The paper introduces Model Disassembling and Assembling (MDA), a novel method inspired by the biological visual system to create new deep learning models without retraining. By disassembling pretrained CNNs into task-aware components and reassembling them, the approach maintains performance while enabling efficient model reuse. Experiments show that the reassembled models match or exceed the original models' performance, highlighting MDA's potential for applications like model compression and knowledge distillation.

STRENGTHS:
1. The proposed method allows for arbitrary assembly of new models from task-aware components, similar to building with LEGO blocks, which is novel and interesting.

2. MDA creates new models without requiring extensive retraining, saving significant computational resources.

3. The experimental results are impressive. The reassembled models can match or even surpass the performance of the original models.

WEAKNESSES:
1. The computation of contributions and component locating may introduce additional time and computational cost, especially when the number of classes or tasks are large. It is recommended to add a analysis of the complexity of this method.

2. Is this method applicable to transformer models and large models?

3. In some cases, the performance of MDA can even outperforms the base model, why?

4. Some typos in writing should be corrected. For example, in Section 5.2.1, the quotes `’sth’` are all right quotes.

QUESTIONS:
See Weaknesses.

LIMITATIONS:
The authors have adequately discussed the limitations.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 4
DATE: 2024-07-14