Model LEGO: Creating Models Like Disassembling and Assembling Building Blocks
Authors: Jiacong Hu
Jing Gao
Jingwen Ye
Yang Gao
Xingen Wang
Zunlei Feng
Mingli Song
Venue: NeurIPS 2024 poster (2024)
Keywords: Disassembling Models
Assembling Models
Model Reuse
Model Interpretability
Primary area: machine_vision

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
Jiacong Hu .Jing Gao', Jingwen Ye², Yang Gao', Xingen Wang!,, Zunlei Feng Mingli Songl.56
CColleee of Computer Science and Technology. Zhejiang University, 2 Robotics Institute, School of Computer Science, Carnegie Mellon University,
3 Electrical and Computer Engineering, National University of Singapore
"Scoool of Software Technology, Zhejiang University.
State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security, 'Bangsheng Technology Co., Ltd.
j jiaconghu@zju. edu, en, jiginggo22gandrr cmu edu, j jingwenydnus edu. sg, (roygao, newroot, zumleifeng brooksong)
orookoog @zju. edu. cn
Abstract
With the rapid development of deep learning, the increasing complexity and scale parameters make training a new model increasingly resource-intensive In this paper, start from the classic convolutional neural network (CNN) and explore 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, draw inspi- ration from the information subsystem pathways in the biological visual system and propose Model Disassembling and Assembling (MDA). During model disassem- bling, we introduce the concept of relative contribution and propose a component locating technique extract task-aware components from trained CNN classifiers. For model assembling, we present the alignment padding strategy and parameter scaling strategy construct new model tailored for 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 assem- bled 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:
gi github. io/.
1 Introduction
Convolutional Neural Networks (CNNs), as the predominant architecture deep learning, play 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 to explore and leverage similar characteristies 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.
Corresponding author.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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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 As- sembling (MDA), novel approach to construct and combine subsystems with LEGO-like flexibility, The conceptual framework behind MDA posits that, much like assembling and disassembling LEGÖ structures, deep learning models can undergo such operations without incurring significant training overhead or compromising performance. This task 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, 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 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 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 parameter scaling strategy. The resulting MDA framework facilitates recombination among different pre-trained models, providing vital technique for model reuse.
Our contributions in this study are summarized as follows:
We introduce novel task, MDA, which aims 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 CNN classifiers. In the model disassembling phase, we introduce a component locating technique to disassemble task-awar aaske 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 bascline models.
MDA introduces 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, present the definition of Model Disassembling and Assembling (MDA). Let us consider set of N pre-trained deep learning models denoted as {MM(")) 1 Each model ((") comprises K (n) subtasks, represented as ( ((()
(n))
jk=1 Our objective in model disassembling is to
extract the model components ( corresponding to specific subtask (n) from the source

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model M()), These extracted components M((g")) are intended to encapsulate only the parameters critically relevant to the subtask
{(m)_ In essence, the disassembled components M ) should
kk
function as an independent model, preserving the full capability for the subtask ( without redundant capability for other subtasks. Furthermore, the assembling process involves combining disassembled components from different models. For example, combining components M(1 3-.-. "k ((x=)) from
(122) from M(rx) results in new model M( This assembled ( and M(rlas) ..... "kk
model M(new) Mi(h (m1) (n1) (m2)
[m2)] is expected retain full functionality for the ko lka 3.... ka
subtasks (th) kk
(m1) and (1/2) .kk
(m2)
MDA is applicable to various existing deep learning architectures, including Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), and Transformers. The focus of this paper is to explore the implementation and efficacy of MDA specifically in the context of CNN classifiers.
3 Model Disassembling
3.1 Contribution Aggregation and Allocation
Given a CNN classifier with K cate- gories, each category is treated as an individual subtask within the classi- fier. Conventionally, Softmax op- eration is employed on the output features of the final fully connected layer, transforming these features into a probabilistic distribution that sums to unity. The feature with a relatively higher value corresponds probability, and the category with the highest probability serves as the pre- dicted result of the classifier. Con- sequently, the feature exhibiting rela- tively greater magnitude plays de- cisive role in determining the final classification result. We term the rela- tive degree to which features influence the result as relative contribution. It is crucial to note that the concept of relative contribution is not confined solely to the final layer of the network. Features from preceding layers play a pivotal role in shaping the features of subsequent layers. Consequently, extend the concept of relative contribution to encompass all layers of the CNN classifier, thereby establishing comprehensive contribution system that spans the entirety of the network.
Up
a1
P
!
i
higher
10
A2
A
@+1
a2
(G+1)
(I+1)
and denotes the most relevant feature maps or par- ameters specifie task
(++1)
((+1)
(1+1)
(u) Contribution Aggregation and Allocation
(b) Component Locating T'ochnique
Disassembling process at the I-th layer of a CNN model, where the red solid line represents the contribution aggregation process, and the black dashed line represents the contribution allocation process.
Figure 1:
To illustrate this process more specifically, we focus on the I-th convolutional layer of a CNN (the case of the fully connected layer is discussed in Appendix A). The (-th layer has P input channels and Q output channels. Correspondingly, the 1-th layer comprises & convolution filters, denoted as (C!") 1 Each filter ci consists of P convolution kernels, thus Clfl {cqpppp= pp=1 The input feature maps for the 1-th layer are represented as {ap Jp=1- With the convolution filter c the
hidden feature maps (1) for the q-th channel in the 1-th layer are computed as follows:
[a
(1) y 1p o.
! & ! 7.P
(I)
where & denotes the convolution operation. From these hidden feature maps Ag the output feature (I+1) in the I-th layer (which serves the input feature map dg map dg
((+1) in the 1)-th layer) is
3

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determined as:
(1+1) ag
Σ
(1) a.p + b/y p=1
(2)
where by) the bias for the q-th channel. From (qn. is evident that the output feature map ag (1+1) is the summation of the hidden feature maps Ag This implies that the value of dg (!) influenced by the value of each individual hidden feature map dgpp. Similar to the Softmax operation in the final layer, the larger the individual hidden feature map, the greater its contribution to the output ((+1) dq
(++1) is
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 {ap }p= are aggregated to the hidden feature maps (1) (1) IP
via the convolution filter c( of the q-th channel. To quantify the contribution of each hidden feature (1) map ag. to the output 0q.
(t+1) we introduce a metric sy.p defined as follows:
(1)
H w(!
a (1) 4.p [h, w).
1= ==1
where H and ('O) denote the height and width, respectively, of the feature map agp.. The term (h, w| represents the pixel value at the /-th row and with column of dg.p- Considering the presence of activation functions such as ReLU, negative contributions are treated having zero
(1)
impact on the result. Consequently, the contribution <(0) of the hidden feature map dq.p is recalculated as:
a.p
( max (s 0). 4.p'
(4)
In line with the principle of the Softmax operation, the contribution of each hidden feature map is 2.P
relative. Hence, we employ min-max normalization to obtain the relative contribution y.()) of each (i)
hidden feature map ag.p
(1) $ min (1 {
}==1)
(
(5)
9.P
max 1 1 min( ( pp +E
where is small constant added 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 the layer.
3.1.2 Contribution Allocation
The black dashed line in Fig. represents the process of contribution allocation, where the contribution
from the feature map ap (i) is allocated to various hidden feature maps { g=1 through different
( 10
(1) convolution kernels {cg.p)ę=1- Consequently, the overall contribution of the feature map ap is calculated using the following equation:
(1)
(!)
(6)
9,9"
q=1
In this calculation, similar to the previous steps, negative contributions are considered as zero:
max 0).
Moreover, acknowledging that the significance of each feature map is relative, the relative contribution Tp of the feature map a is computed as follows:
8 min (3)) }p-))
(8)
max (1 11 ) ) min (18)) (s!))p 1 +E

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3.2 Component Locating Technique
Building upon the concept of relative contribution and the mechanism of contribution aggregation and allocation, we introduce novel approach termed the component locating technique. This method designed to identify task-aware components, that is, the parameters most relevant task, within CNN. Taking the 1-th layer as 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.
given
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 is employed to discern whether a relative contribution rg.p (calculated Eqn. 5) is large or small:
T (1)
2
(())
9.P
0
(1) <
4.P
where is chosen value within the range (0,1). Through the above equation, the soft relative contribution (1)) which are continuous values indicating the degree of contribution, are transformed into hard relative contribution (!!) 9..0 Eqn.7 and Eqn.8 remain unchanged.
(1) in Eqn. 6 is now the sum over the hard relative contribution 4.p- The Sp
Further, a second threshold value 33 is used to determine whether the relative contribution r defined in Eqn.8) of feature map is large small:
(as
1
2
(
p
(10)
0
<
where 3 is a chosen value within the range (J. This equation transforms the soft relative contribution (1) rp into hard relative contribution ((8) just as in the case of (!))
992-
3.2.2 Parameter Linking
In Eqn. 10, the hard relative contribution (!)) being either 0 or 1, indicates whether the feature map (1) ap is most relevant to the predicted result. Similarly, the hard relative contribution s((+1) can also
reflect whether the feature map a, (1+1); is most relevant the predicted result. Integrating Eqn. with Eqn.2, we can represent the convolutional process as follows:
((+1)
(2)
(11)
4
P
This equation signifies that the output feature map 0g
(1+1) is generated by the convolution operation of
the input feature maps (ap (1) pp=1 with the respective convolution kernels {c4.p)p=11 which collectively form the convolution filter C(O) Thus, if the output feature map a. (I+1) is determined to be most relevant to the predicted result, then it logically follows that the convolution filter c() and the associated bias 6(") also most relevant to the predicted result. Furthermore, each input feature map ( engages in the convolution operation exclusively with the kernels {cg. ) across different convolution filters. Therefore, the feature map ap is identified as most relevant to the predicted (1) result, then the convolution kereels {cq.p]qm1 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 specific task. This includes pinpointing the most relevant filters, kernels, and biases. Subsequently. the identified parameters most relevant to specific task can be extracted from the source model through process known structure pruning [18].
5

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Model Assembling
In the process of CNN model sembling, our objective to com- bine disassembled, task-aware ponents, typically derived from differ- ent source models, into new model. This assembling is performed with- out necessitating retraining or incur- ring performance loss. To achieve this goal, we propose alignment padding strategy and a parameter scal- ing strategy. It is important to note that the focus of this paper is specifi- cally on assembling different models that share isomorphic network archi- tectures.
(4)
(c)
e0
J
((+1)
(++1) d
Figure 2: Assembling process at the I-th layer of CNN mod- els: (a) and (b) represent two distinct disassembled models, respectively; (c) illustrates the assembled model.
4.1 Alignment Padding Strategy
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 simple yet effective alignment padding strategy. This strategy involves padding empty kernels to each filter, ensuring that all filters in given layer have uniform number of kernels.
Consider the I-th layer example. As depicted in Fig. 2(a), a disassembled model consists of J convolution filters, denoted as (C,"" 1 Each convolution filter c comprises I convolutional kernels, expressed as ((I) (1!)) 1/-1 Another disassembled model, shown Fig.2(b), contains convolution filters {C Jv=1 with each filter CºO consisting of convolutional kernels, expressed as C (ceu V Ju= 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 augmented with a complementary number of empty kernels. Specifically, if a convolution filter (()) originates from C/P it is padded with U empty kemelss at the end, forming C/ (c
(1) S.r. 02, ........ ) Conversely, if convolution filter ((I) comes from cxl) it is padded with / empty kernels at the beginning, resulting in E()) {01, 02, .2, O2, 1, 2.......:
(1) .... ".......
(!) c. Through this alignment padding strategy, every convolution filter in the assembled model is standardized have the same total of I I+ U convolution kernels.
(!) (1)
The alignment padding strategy can be readily extended to incorporate multiple disassembled models and applied across all layers a CNN. A key feature of this approach is that the assembled model ready for inference immediately, without the need for any retraining.
4.2 Parameter Scaling Strategy
In addition 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 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 1-th layer as an example, as shown in Fig. and (b), let's consider the output feature maps ((+1)
in the 1-th layer of the disassembled models, denoted as {a, (++1) = and {a ) Jv= respectively.
(I+1) is quantified follows: The magnitude of each feature map 0
01+1 w(+++)
of ((1)
Σ
((+1)
[h, w).
(12)

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Table 1: Comparison of disassembling performance. In the classifier, each category corresponds to task. 'Base.' refers to the average accuracy for 'Disassembled Task' in the source model. In 'Disa.. 'Scorel (+Score2) represents two metrics: 'Scorel' is the aceuracy and 'Score2" is the improved accuracy compared to 'Base.' for the 'Disassembled Task' in the disassembled model.
Disassembled
VGG-16
ResNet-50
GoogleNet
Dataset
Task
Base, (%)
Disa. (%)
Base. (%)
Disa. (%)
Base. (%)
Disa. (%)
0
94,40
100.00 (+5.60)
96.10
100.00 (+3,90)
95.40
100.00 (+4.60)
1
96.50
100.00 (+ 3+3))
96.20
100.00 (+3.80)
97,40
100,00 (+2.60)
CIFAR-10
01-
93.87
95.47 (+1.60)
94.93
97.43 (+2.50)
94.47
98.17 (+3.70)
3-9
92.49
92.27 (-0.22)
93.81
94.46 (+0.65)
93.46
93.17 (-0.29)
0
84,00
1000000 100.00 (+16.00)
92.00
(+8.00)+
90.00
100.00 (+00000)
87.00
100.00 (+13.00)
$7.00
100.00 (+13.00)
85.00
100.00 (+15.00)
CIFAR-100
0-19
71.05
82.50 (+11.45)
75.55
77.15 (+1.60)
75.90
87.55 (+11.65)
20-69
72.74
79.66 (+6.92)
77.68
79.72 (+2.04
76.60
82.42 (+5.82)
0
82,00
100.0
92.00
100.00 (+8:00)
88.00
1100000 (+12.005
70,00
100.0 (+30,00)
80.00
100.00 (+20.00)
76.00
100.00 (+24.00)
Tiny-ImageNet
0-69
50.17
55.49 (+5.322)
56.06
56,40 (+0.34)
52.89
59,40 (+6.57)
70-179
45.36
47.95 (+2.59)
51.27
53.42 (42.15)
47.91
51.04 (43.13)
Similarly, the magnitude e compute the average magnitude of the feature maps from all disassembled models in the I-th layer:
!!+1) for feature map du
(L+1), is calculated using the same equation. We then
V
e(++)
Σ
(13)
J+V V
mml
Then, the convolution filter G.P is scaled as follows:
= /e
(14)
In practical applications, this parameter scaling strategy particularly crucial in the last fully connected layer ensure that the magnitude differences in the final outputs of disassembled models from different source models are not excessively large.
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- [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 a and B, defined in Eqn.9 and Eqn. 10, respectively. By default, we set = 0.3 and / 0.2 in convolutional layers, and a 0.4 and 8 0.3 in fully connected layers, unless specified otherwise. The model training is conducted using the SGD optimizer, with learning rate of 0.01. To ensure the reliability and reproducibility of our results, we report the average three independent experimental runs for each result. Comprehensive details and the source code can be found in the Supplementary Material.
5.2
MDA Applied to CNN Models
5.2.1
Model Disassembling Results
We present the results of model disassembling in Table The results in Table reveal that both single-task and multi-task disassembling, as executed by our method (denoted '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 -mag-em on ResNet-50 showed improvement of over 15% compared to the source model. What's more, to
7

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Table 2: Comparison of assembling performance. 'Base.' indicates the average accuracy for the "Assembled Task' in the source models. 'Asse., 'Score/ / Score2' represent the average accuracy scores for the 'Assembled Task' in the assembled models without fine-tuning and with ten epochs of fine-tuning, respectively.
Assembled
VGG-16
ResNet-50
GoogleNet
Dataset
Task
Base, (%)
Asse. (%)
Basc. (%)
Asse. (%)
Base. (%)
Assc. (%)
0 0
89.20
87.00 88.43
94.05
77.30 87.36
99.60
94.25 / 195.27
CIFAR-10
0-2-00119
74.03
74.17 74.19
78.08
64.34
76.37
78.32
79.22 / 79.22
+
3-9 20-69
75.16
73.72 74,25
79.66
72.03 75.25
78.67
70.24 / 76.37
CIFAR-100
0.9 20-99
74.87
72.07 73.118
79.54
65.97 / 74.65
78.57
66.59 / 70.36
0+0
88.20
04.70/90.72
94.05
86.95/94.51
91.70
62.407 70.40
CIFAR-I0
0-69
51.97
/
57.65
43.09 56.38
54.59
57.51 57.81
+
0-69
54.02
50.20 52.48
52.44
59.49
58.14/58.63
56.57
53.74 55.32
Tiny-ImageNet
0-9 70-179
49.33
42.30 47.98
$4.85
47.21/55.11
51.73
48.00 / 52.16
0000
83.00
50.00776.28
92.00
53,00 87.34
89.00
75.228
CIFAR-100
0-19 0-69
69.86
50.66/
74.59
57.86/ 69.23
74.73
58.67 69.14
20-69 70-179
71.48
65.71
76.54
56.53 69.79
75.08
54.09 77.27
Tiny-lmageNet
0-99 0-199
55.97
/ 56.13
61.51
53.05 57.23
59.19
48.66 / 58.28
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. 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 10 Imay-ImageNet' However, there are instances, such as with '20-69 + 70-179 from 'CIFAR- 100 + Tiny-ImmaeNet' on ResNet-50, where decrease in accuracy is noted. This could be attributed the interaction and interference among the numerous parameters from the different models being assembled, particularly when the number of tasks is large, leading less stable predictions in the model.
GoogleNet surpass the source model in terms of accuracy.
5.3 MDA Applied to GCN Model
The proposed MDA method extends be- yond CNN models and is equally appli- cable to Graph Convolutional Network (GCN) models [25]. We demonstrate this by conducting a disassembling exper- iment for node classification using GCN model [25] on the Cora dataset [26]. The results of this experiment are presented Table 3.
Table 3: Performance the GCN model disassembling on the Cora Dataset. 'Base.' represents the average ac- curacy for the DDssassmbled Task' the source model. In 'Disa',, 'Score (+Score2)) indicates the accuracy and the improvement accuracy ("Score2") compared to 'Base.' for the 'Disassembled Task' of the disassem- bled model.
Disassembled
GCN
Dataset
The results clearly indicate that the accu- racy of the disassembled GCN model sur- passes that of the source model. For in- stance, the accuracy of the disassembled model for categories '1-2' shows an im- provement of 1.47% over the source GCN model.
Task
Base. (%)
Disa. (%)
0
72.36
100.00 (+27.36)
78.54
97.10 18.56)
Cora
88.27
89.74 (+147
3-5
80.23
81.34 (+1.11)
5.4 Ablation Study
Parameters and 3. Fig. 3 presents an ablation study on the thresholds and in the fully connected layer and convolutional layer. With the increase of the thresholds and 33, fewer parameters will be regarded as relevant to the specific tasks. Therefore, in Fig. 3(a), observe that as the

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1.00
1.0
0.55
97
1 8 & 8
0.45
accumey
FLOPs
0.7
par snneeeeeer
ATFO radio
sccuracy
0..
FLOPS
0.40
size rubio
0.4
radio
0.35
0.30
0.30
0.25
(8)
basc:
(c)
10
10
0.0
0.15
Figure 4: Accuracy curve (a), FLOPSs ratio curve (b), and model parameter size ratio curve (c) for the disassembled model as the number of disas- sembled layers varies. The number of disassem- bled layers accumulates from deep layers to shal- low layers in the model.
Figure 3: Accuracy curve (a), FLOPs ratio curve
(b), and model parameter size ratio curve (c)
for the disassembled model, varying with hyper- parameters the fully connected layers 3g) and convolutional layers (a 3c).
thresholds a and increase, the accuracy of the disassembled model decreases, with thresholds the convolutional layer being more sensitive than in the fully connected layer. Fig. 3(b) illustrates that thresholds a and 3 induce more significant changes FLOPs in the convolutional layer compared to the fully connected layer. In Fig. 3(c), the model parameter size ratio of the disassembled model decreases with the increase in thresholds.
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 FLOOPs 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 de- tailed in Table 4. It is observed that the method combining padding align- ment 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 significant accu- racy increase of 37.00%. These re- sults affirm the effectiveness of the assembling strategies proposed in this paper.
Table 4: Comparison of assembling strategies. 'Base.' repre- sents 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 pa- rameter scaling strategies, respectively.
Assembled
VGG-16
Dataset
Task
Base.
+Padd.
+Padd. +Para.
0+0
89.20
50.00
87.00
CIFAR-10
0-2 + 0-19
74.03
71.74
74.17
20-69
+ 20-69
75.16
71.97
73.72
CIFAR-100
(-9 + 20-99
74.87
69.12
72.07
Related Works
6.1 Model Explanation
Model explanation methods can generally be categorized into four types: activation-based gradient- based, perturbation-based techniques, and undlag Layer-wise Relevance Propagation (LRP). Activation-basat approaches [27, 28, 29, 30, 31, 32, 33, 34] involve calculating set of weights and then aggregation feature maps to highlight crucial features. Gradient- methods [35, 36, 37, 38, 39, 40, 21, 41, 42] utilize gradients to identify key features. Perturbation-base
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, LRP-e, LRP-7, LRP-a,3, flat, wu-rules, and = -rule, and discussed their distinctions and interconnections. Additionally, Ancona et al. [49] examined various gradient- techniques (such as Gradient Input, Integrated Gradients, and DeepLIFT) and LRP from both theoretical and practical angles, highlighting their similarities and

---

conditions for equivalence or approximation. While these model explanation techniques are primarily employed locate important features in the original imput contributing 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-hoe techniques. Ante- hoc techniques, such those proposed by Li et al. [50] and Liang 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 [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 verturbation-basses 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 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 I of the main text, demonstrate that models disassembled using the proposed method can surpass the source model in of accuracy. However, the performance of the assembled models, as shown in Table 2 of the main text, indicates decrease in accuracy in certain cases (e.g., '0-19 0-69', '20-69 70-179', '0-99 + 0-199' for 'CIFAR- + Jiny-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 models in other domains, including object detection and segmentation.
Conclusion
In this paper, we introduce 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 playing with LEGOs. The primary focus of this paper centers the application of MDA to CNN classifiers. During model disassembling, we introduce the concept of relative contribution and propose component locating technique extract task-aware components from trained CNN classifiers. For model assembling, we introduce the alignment padding strategy and parameter scaling strategy construct 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 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.
10

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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).
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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 Jq-1- each comprising P kernels ( {< pp=1- Notably, in the FC layer, both kernels and features arc single real numbers, i.c., ap and Cg.p IR, contrasting with the convolutional layer where kemelss and features are two-dimensional matrices of real numbers, denoted as E TR: #R and (!) E R xw; (with 17 and W representing the height and width of features, and 11 and ((()) representing the height and width of kemels).
(0)
Given this structural discrepancy, the method for computing contributions in fully connected layers requires adaptation. Specifically, the contribution $q.2 defined in E3n.(3) can be directly equated to the hidden feature 0g.0 as expressed helow:
(15)
The remaining equations in the main text remain unchanged.
ap
A
(
A
(
(++1)
(++1)
(1+1)
22
Q
Figure 5: The process of contribution aggregation and allocation at the 1-th fully connected layer, wherein the red solid line delineates the contribution aggregation process, and the black dashed line signifies the contribution allocation process.
B Component Locating Technique for a Specific Task
Figure 6 illustrates visualization of the soft relative contrihution rp for input feature maps in an intermediate convolutional layer of a model. Additional visualizations of relative contribution are available in ş1. Notably, in Fig. 6(a), 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 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, 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. essence, each category linked to a unique set of convolution filters and kernels.
Therefore, considering the I-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
(!) in the (-th layer employs a The computation of the relative contribution (!)) for the input feature map backward approach. Consequently, it is imperative to account for the relative contribution of the input feature maps in the subsequent (1 + 1)-th layer when computing the relative contribution in the (-th layer. Specifically,
14

---

cillegrry 0.
-00 100 1.0 200
2.50 300
350 400
450
500
chamnel of filler
Figure 6: Visualization of the soft relative contribution T' (as defined in Eq).(8) in the 13-th convolutional layer of the VGG-16 model trained on the CIFAR-10 dataset. Subfigure depicts different input samples belonging to the same category, while subfigure (b) showcases input samples from different categories. Bright and dark colors respectively represent large and small values of relative contribution.
19=1 of the I-th layer. Therefore, relative contribution (Fi (+((+1)) 1 of these output feature maps I-th layer is equivalent to relative contribution of the input feature maps in the (I 1)-th layer.
the input feature maps the (1 1)-th layer correspond to the output feature maps faş (1-1)
In accordance with Eqn.(2), if the relative contribution F. 1+(++) for the output feature map 0 (1+1) is zero, then the (0)
relative contribution [r of the hidden feature maps {0g.php-1 also considered as zero. Consequently,
in (9), the hard relative contribution e(!) for the hidden feature map ag.p is recalculated as follows:
r 4. 2 a and p((+1)
(16)
q.P
(I) T 4.P
< or F
0
The remaining equations in the main text remain unchanged.
(a)
1
alignment padding strategy
concatenate
component
component B
(b)
concatenate
2 alignment padding strategy
component
component B
Figure 7: (a) The default alignment padding strategy and (b) the improved alignment padding strategy in the inception module.
15

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D Alignment Padding Strategy for Inception Module
Due to the distinctive multi-branch concatenation architecture, specifically the inception module, 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 heing dispersed throughout the channel various locations after the concatenate operation. In our improved alignment padding strategy, depicted in Fig. 7(b), the alignment padding applied after the concatenate operation.
E More Experiments of CNN Model Disassembling
To further investigate the efficacy of the proposed disassembling method, we present parameter size and Floating Point Operations Per Second (FLOPs) in Table 5. 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.
Table 5: Comparison of disassembling performance using additional metrics. In 'Para.', 'Score I / Score2' represent the parameter sizes (in millions, M) for the 'Disassembled Task' in the source and disassembled models, respectively. In 'FLOPs', 'Scorel Score2' indicate the FLOPs (Floating Point Operations per Second) for the 'Disassembled Task' in the source model and the disassembled model, respectively.
Disussembled
VGG-16
ResNet50
GoogleNet
Dataset
Task
Para.
FL.PPs
Para.
FLOPPs
Para.
FL.OPs
0
33,65 /4559
33.29/ / 12,31
23,52/ 13.77
130.47/ / 105.00
6,319
/5.43/46.40
33.65 / 2.28
23.52/ 10.32
130.47 / /92.52
6,31
/4..8/44.81
MNIST
0-2
33.65/9.66 /
23.52 18.39
130.47 122.11
6.31/5.30
55.43/49.04
3-9
33.65 14.84
03.29/30.77
23.52/21.85
130.47
126.62 6.31/5.73
/5.44/
0
130477113.27
633174.75
33.65 3.17
/
23.52
12.18 130.47 /94.90
6,31/4.53
/5.43/45.91
PASHION-MNIST
0-2
33.65 10.97 33.29
22.79
23.52
18.90
130.47
122.49
6.31/5.31
55.43/48.84
33.65 15.27
33.29
21.72
130.47/127.6
6.71/
55.43/49.12
3..65/2.13
"33.2978335
23.5278.11
130.47756.33
6.31739955
55,43737.72
2.365
13.29/11.88
23.52/ 8.1
130.47
77.19
6,31
45.49
CIFAR-10
0-2
33.65 /
33.29
18.69
23.52/
15.55
130.47
106.51
6.31/5223
45.43/45.50
3-9
33.65 13.48
33.29 27.36
23.50
20.70
130.47
116.74 6.31/5.73
46.73
34,02/3.09
33..33711.2
23.7173.20
1300.9973.66
6.4073166
55.44733.13
3..02/3.11
33.33/
12.73
23.71 6.188
130.49
55.76
6.20
/5.44/35.43
CIFAR-100
0-19
34.02 00000/2660 26.81
33.33/ 30.76
23.71 / 17.45
130
109,51
6.40/5.29
45.22
20-69
34.02 /0.00/33000 30.01
33.33/31.97
23.71/21.56
130.49
120.01 6.50
45.44/47.08
14437/663
"53.3776.62"
2339175110
13003174998
63117'111
53:45335338
Imageel
34.43 1.58
34.67
23.91/4.41
130.51/
38.59
6.51/3.13
55.45
0-69
34.43 29.40
33.07/32.07
23.91 22.43
130.51
126.98
6.17
55.45/ 50.80
179
34.43 30.72
33.37
23.91 23.22
130.51 / 128.33 6.51/6223 /5.45/51113
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 Ting-ImaeeNet 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.
VGG-16 are less than those for
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 thase without assembling. For instance, the accuracy of *0-2 0- from CIFAR-10 C10AR-100 on VGG16 6 decreases after assembling. This decline in accuracy may be attributed the possibility that samples from specific category could inadvertently attain high confidence other unrelated components of the assembled models. A notable example involves similar features across different categories, such monkey (from model ())) and gorilla
16

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Table 6: Performance comparison between models with and without assembling. 'Ma) and (M(2) represent the accuracy of the two disassembled models in the 'Assembled Task', respectively, In 'Asse.' 'Scorel Score2' indicate the average accuracy scores for the 'Assembled Task' in the assembled models, presented without fine-tuning and with ten epochs of fine-tuning, respectively. All results are expressed as percentages.
Assembled
VGG-16
ResNet50
GoogleNet
Dataset
Task
M
(2
Asse.
M!
M2
Asse.
M
MM
Assc.
MNIST
+++
100.00
100.00
97.35
99.41
100.00
100.00
62.05
100.00
100.00
96.05
96.12
0-2
99.94
97.90
98.48
/98.48
99.97
98.00
90.47/94.
94.14
100.00
97.57
98.27 98.16
FASHION-MNIST
4-2
95.57
95.62/
99.97
95.66
88.58
/93.20
100.00
96.44
96.34
0.2
99.68
97.90
98.29 98.26
99.62
98.00
83.97
/9.557
95.57
99.74
97.57
98.49 97.49
"1'00""
106.00
100300
5000078524
1000.000
1001000
17.30787..38
100.00
100.00
94:15796227
CIFAR-10
0-2+0-19
79.22
20-69
92.27
79.66
73.72 74.25
94,46
79.72
72.03 75.25
93.17
82.42
70.24 76.37
CIFAR-100
91.37
72.54
72.07
73.18
92.32
76.39
74.65
92.38
78.04
66.59 70.36
"1400"
100.00
T00300
914,70
T0000001
1001.00
100000
TTROT
62:40780:344
CIFAR-10
0-29
99.94
$5.49
53.20 53.20
97.43
56.40
43.09
56.38
98.17
59.40
57,51 57.81
Tmy-IIageNNet
3-9 3-69
92.27
55.49
50,20 52.48
94.46
56.40
52.14/ 58.63
93.17
59.40
53.74 55.32
0-9 70-179
91.37
47.95
42.30 47.98
92.32
53.42
47.21 55.17
92.38
51.04
48. 00 52.16
0''000 --------------------------------
100.00
100:00
30.007
76.28
100.000
100.00
53.00787.33
100:00
100.00
000000785.2
CIFAR-100
0-19 0-69
57.88 87.86/ 69.23
87.55
58.67 69.14
Timy-ImageNet
20-69 4 70-179
79.66
47.95
50.08
65.71
79.72
53,42
56.53 69.79
82,42
$1.04
54.09 71.27
0-99 + 0-199
71.38
47.41
43.06/ 56.13
73.97
53.51
53.05
57.23
76.08
50.53
66/ 58.28
Table 7: The disassembling performance on ImageNet with VGG-16. 'Base.' denotes the average accuracy for specific categories from the source model. For 'Disa', "Scorel (+Score2)* denotes the average accuracy 'Scorel' (the improved average accuracy 'Score2' compared to 'Base.) for specific categories from the disassembled model.
Disassembled
VGG-16
Dataset
Task
Base. (%)
Disa. (%)
0
90.00
100.00 (+ 10.00)
1
90.00
100.00 (+10.00)
0-9
81.20
83.37 (+2.17)
ImageNet
0-99
76.06
79.24 (+3.18)
0-299
75.84
77.23 (+1.39)
100-499
73.15
74.38 (+1.23)
500-999
66.31
67.34 (+1.03)
(from model ()), In such cases, a sample from one category might receive a higher confidence score from the model trained on the other category.
However, key advantage of model assembling lies in the reduction of inference time. As the number of disassembled components increases, running each component independently becomes time-consuming compared to utilizing an assembled model. This underscores the trade-off hetween model performance and computational efficiency in assembling process.
G More Experiments of CNN Model on Large-scale Datasets
is crucial to investigate the scalability of the proposed framework on large-scale datasets, such as Ima- geNet [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 higher by 3.18% compared the source model. These experiments underscore the scalability of the proposed method, which extends its applicability to mainstream benchmark datasets.
H MDA Applied 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- traitre CNN classifiers for specific tasks without
17

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layer
layer 3
layer 4
layer 4
0
layer
layer
layer
layer
layer
layer
0
0
15
15
199
of category dog route of the category automobile
199
Figure 8: Decision routes of the categories 'dog' and 'automobile' in the LeNet-5 model on the CIFAR-10 dataset, where the channels of the 'dog' and "automobile" in layer 1 are the same, while in later layers, such as layer 2, 3, 4, and 5, they are totally different.
Table 8: Comparison of model compression performance. 'Base.' refers the source model, while 'Ours.' represents the model compressed using the proposed method. 'Acc.', 'FLOPs', and 'Para.' indicate the accuracy, floating point operations, and parameter size of the model, respectively. All accuracy scores are presented as percentages.
CNN
Basc.
Ours
FPGM
HRank
Dataset
classifier
Acc.
FL.PPs
Para.
Acc.
FLOPS
Para.
Acc.
FLOPs
Para.
Acc.
FLOPPs
Para.
VGG-16 92,90 33.29
33.65M 91.31 28.00
18.63M
91.58
28.87
26.23M
91.97
20.19
13.25M
CIFAR-10
ResNet50
94.15
130.47
23.52M
93.71
123.21
18.93M
93.24
63.71
12.28M
93.89
71.12
14.42M
GoogleNet 93,76
55.43 6.31M
92.38
47.25 5.78M
93.22
25.21
3.93M
93.13
27.16
4.09M
necessitating additional training. This adaptability extends to various other tasks, including but not limited to model decision route analysis, madel compression, knowledge distillation.
H.1 Model Decision Route Visualization
The concept o 'decision route' refers the specific data flow pathways utilized for each category within deep learning model. These pathways are typically sparse and remain fixed. As elucidated in $B, the prediction for different categories within 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 misclassified sample with corresponding correct category's route, rescarchers 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 examplc, Fig.8 illustrates decision routes for specific categories using LeNet-5 [55], which includes two convolutional layers and three lincar layers. It is observable that each category possesses distinct decision routes, yet there convolutional filters often process fundamental features
overlap among them. This overlap be attributed to the fact that shallower
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-specinn features at deeper levels of the CNN.
In summary, visualizing the decision routes for specific classes within CNN classifier not only aids in dissecting the classifier's underlying mechanisms but also proves instrumental in debugging and enhancing model's performance.
H.2 Model Compression
The proposed MDA presents novel approach to model compression for CNN classifiers. Utilizing our component locating technique, we effectively disassemble sategory-specifie parameters from the source model. This process entails separating parameters utilized across all categories and discarding those that are redundant, ie., not used by any category. Consequently, this method offers unique avenue for model compression.
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Table Comparison of knowledge distillation performance. 'Base.' represents the average accuracy for the 'Assembled Task' obtained from the source models. All accuracy scores are expressed as percentages.
Distilled
VGG-16
Dataset
Category
Base.
Ours
KA
++0
94.60 97.35 92.24
MNIST
96.69 98.48 93.81
+
0-2
96.12 95.62 92.76
PASHION-MNIST
0-9
98.24 98.29 93.57
0+0
89.20 94.00 84.35
CIFAR-10
0-29
74.03 74.17 69.46
+
3-9
75.16 73.72 71.38
CIFAR-100
0-9
74.87 72.07 71.60
"0+0""
88:20 94.10 85:62'
CIFAR-10
0-2
51.97
53.20 52.67
0-69
54.02 50.20 53.26
Tmageem
709199
49.33 42.30 51.84
0+0
83.00
85.50 81.31
CIFAR-100
0-19
69.86
50.66 68.24
20-69 + 70-179
71,48
50.08 67.52
Tmny-lmageNet
0-99 0-199
55.97
43.06 52.74
For empirical validation, we conducted compression experiments using three mainstream classifiers: VGG- 16 [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 art (SOTA) model compression techniques, specifically two pruning based methods: FPGM [57] and HRank [58].
The results, shown in Table 8, indicate that method achieves comparable accuracy the SOTA methods, FPGM and HRank. However, it is observed that method exhibits higher Floating Point Operations Per Second (FI)Os) and parameter size compared to these pruning-hane methods. This distinction likely stems from our method's '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.
future rescarch, 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 assembling task-aware components disassembled from different models into new, unified model. This process hears resemblance to Knowledge Amalgamating (KA) as deserihed in Shen 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 16 [21] model on five benchmark datasets: MNIST [55], Fashion- MNIST [60]. CIFAR-10 [19], CIFAR- 100 [19], and Tiny-ImageNet [20]. The results of these experiments summarized in Table
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 + FASHION- MNIST". Conversely, as the number of assembled categories increases for instance, in the case of 'CIFAR-100 + ImageNee,, the performance of our method tends to decline, even falling below that of the source model and KAA 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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MINST
VGG-16
Calegory
Fadbion MNIST
CIFAR-10
25
99
Tiny-ImageN
49
99
149
199
Figure 9: Visualization of the soft relative contribution Tp (as defined in Eqn.(8) for input samples from different categories in layer 13 of VGG-16 different datasets.
MNIST
Layer $2 of ResNett0
Calegory
Fashion -NIST
CIFAR-10
CIFAR-
49
75
99
Tiny-ImageNet
99
149
199
Figure 10: Visualization of the soft relative contribution - (as defined in Eq).(8) for input samples from different categories in layer 52 of ResNet-50 on different datasets.
I More Visualization of Relative Contribution ($B)
this section, Figs. 9-12 show additional visualization results of the relative contribution. 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. certain instances, such as the case of Layer 52 ResNet50 on CIFAR- 10, distinct categories cxhibit 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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MNIST
Layer 66 of GoogleNet
Catagury
MNIST
CIFAR-10
CIFAR-100
.99
75
99
Tiny-ImageNer
149
Figure 11: Visualization of the soft relative contribution 1 (as defined in Eqn.(8) for input samples from different categories in layer 66 of GoogleNet on different datasets.
21

---

Different Layers of VGG-16
Category
aayer
2ayer2
Layor
aayer
Lyer
uver
Layer
Layer
ayerr to
over 11
Nver 12
ayer 13
Layer Loyer 15
Laver 16
Figure 12: Visualization of the soft relative contribution r/ (as defined in (8)) for input samples from different categories in different layers of VGG-16 on CIFAR-10.
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Reviews:
[{"review_id": "GIRq7wKfBm", "reviewer": "Reviewer_FhUd", "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.", "questions": "See Weaknesses.", "limitations": "The authors have adequately discussed the limitations.", "rating": 7, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "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.\n\n2. MDA creates new models without requiring extensive retraining, saving significant computational resources.\n\n3. 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.\n\n2. Is this method applicable to transformer models and large models?\n\n3. In some cases, the performance of MDA can even outperforms the base model, why?\n\n4. Some typos in writing should be corrected. For example, in Section 5.2.1, the quotes `’sth’` are all right quotes.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your detailed review and comments on our work. We are pleased that you found our work to be novel and interesting, and that you found the experimental results impressive. We are also delighted that you pointed out how our method allows for the arbitrary creation of new models, similar to playing with Lego bricks, significantly saving computational resources. Below are our responses to each of your comments (your comments are highlighted in italics).\n\n> Q1: *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.*\n> \n\nWe greatly appreciate your concern regarding this issue. The proposed method indeed introduces additional computational costs. Specifically, let $K$ represent the number of classes or tasks, and $d$ represent the total dimension of features in the network. The complexity of our method consists of the following components:\n\n- Contribution Aggregation and Allocation: The computational complexity for this part is $O(K \\cdot d)$.\n- Component Locating: The computational complexity for this part is $O(K \\cdot d)$.\n\nAlthough the proposed method introduces the aforementioned computational costs, these steps are essential for accurately identifying the parameters relevant to the subtasks. Moreover, compared to training a model, the time overhead required for these steps is almost negligible.\n\n> Q2: *Is this method applicable to transformer models and large models?*\n> \n\nThank you for your question. Here is our response:\n\n**Applicability to Transformer Models**: The proposed MDA method, including the underlying concepts of contribution aggregation and allocation, is indeed applicable to Transformer models. The method is not tightly coupled with CNNs and can be applied to linear layers and other structures beyond convolutional layers (Appendix C), making it a general paradigm for model disassembly and reassembly tasks. This is demonstrated in the experiments presented in Section 5.3 of the paper. Additionally, we conducted a preliminary model disassembly experiment using ViT-Tiny on ImageNet-10, and the results are shown in Table 1.\n\nTable 1: Disassembly performance of ViT-Tiny on ImageNet-10.\n\n| Disassembled Task | Base(%) | Disa(%) |\n| --- | --- | --- |\n| 0-1 | 87.70 | 89.25 (+1.55) |\n| 1-3 | 75.10 | 78.10 (+3.00) |\n| 3-9 | 77.51 | 79.29 (+1.78) |\n\nAs shown in Table 1, the disassembled model remains functional for inference, with accuracy surpassing that of the original model.\n\nAdditionally, we tested the assembly performance of ViT-Tiny on CIFAR-10 and CIFAR-100. However, the results were not satisfactory. For example, when assembling disassembled CIFAR-10(0-3) and CIFAR-100(11-17) models, the accuracy without fine-tuning was only 13.27%. The primary reason for this is the task interference that occurs when submodels from different source models are assembled, a phenomenon that is particularly severe due to the self-attention mechanism inherent in Transformers. We believe that adjusting parameter distribution from different submodel parameter spaces before assembly could be a potential solution to this issue, which will be a focus of our future work.\n\n**Applicability to Large Models**: Applying MDA to large models, such as GPT models based on decoder-only Transformers, is feasible and is a current and future focus of our research. For large models in tasks like natural language generation or question answering, it is crucial to clearly define subtasks. Our current approach involves defining different factual knowledge (e.g., \"The Eiffel Tower is located in Paris\" and \"The first Olympic Games were held in Athens\") as subtasks. Architecturally, the primary parameter layers in Transformers are linear layers, and our MDA method's contribution aggregation and allocation are applicable to these layers. The main challenge lies in addressing the task interference issue mentioned earlier.\n\nIn conclusion, we fully agree on the importance of the question you raised. We believe that the proposed MDA method can serve as a general paradigm applicable to various neural networks, and advancing the disassembly and reassembly of models on Transformers and large Transformer-based models is a key focus of our ongoing and future research efforts.\n\n> Q3: *In some cases, the performance of MDA can even outperforms the base model, why?*\n> \n\nThis is an excellent question. The superior performance of the proposed MDA method over the base model can be attributed to two main factors:\n\n1. The source model, from which the disassembled components are derived, can be considered as trained on additional data relative to the target task. This means it has learned more features and possesses better feature extraction capabilities.\n2. More critically, during model disassembly, our proposed method extracts only the parameters relevant to the subtasks, discarding those unrelated. This means that during inference, there is no interference from parameters associated with other subtasks, leading to higher inference accuracy.\n\nThe performance improvement demonstrates that disassembling and identifying subtask-related model parameters is both feasible and effective. Besides the accuracy improvement, the experiments in our paper also show that our method accurately identifies subtask-related parameters and effectively removes irrelevant ones (as detailed in Table 5 of Appendix G).\n\n> Q4: *Some typos in writing should be corrected. For example, in Section 5.2.1, the quotes `’sth’` are all right quotes.*\n> \n\nThanks for pointing it out. We have corrected the typos in the paper and thoroughly reviewed the entire document.", "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": "", "extra_scores": {}}, {"review_id": "wKgtCWACe8", "reviewer": "Reviewer_UTXG", "summary": "This 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.", "questions": "Could 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?", "limitations": "As 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.", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The idea presented by this paper is novel and the technique adopted for solving the problem is new.", "weaknesses": "1. 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your review and comments. We are pleased that you found our work to be novel and our techniques for solving the problem to be innovative. Below are our responses to each of your comments (your comments are highlighted in italics).\n\n> Q1: *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.*\n> \n\nThank you for your constructive comments. We highly value the points you raised and provide our responses below.\n\n**On the Definition of Sub-tasks**: The definition of sub-tasks in our model disassembly and reassembly is not tightly bound to categories. Beyond classification tasks, sub-tasks can be defined in various ways, including:\n\n1. Image-related Sub-tasks: Besides classification, sub-tasks can extend to other image-related tasks, such as object detection and image segmentation. For instance, sub-tasks could include detecting/segmenting \"pedestrians\" or \"vehicles.\" In image generation, sub-tasks could involve generating \"eyes\" or \"noses.\" Therefore, the concept of model disassembly and reassembly is not strictly limited to classification tasks but has potential applications in detection, generation, and more.\n2. Natural Language-related Sub-tasks: Additionally, sub-tasks can be defined for natural language processing tasks. In tasks like natural language generation or question answering, factual knowledge such as \"The Eiffel Tower is located in Paris\" can be defined as a sub-task. Queries like \"Where is the Eiffel Tower?\" or \"Is the Eiffel Tower in Paris?\" fall under this sub-task. Other factual knowledge, such as \"The capital of the UK is London,\" can also be defined as sub-tasks. Hence, the ideas of model disassembly and reassembly have potential applications in NLP models, including large language models, and can be used for learning or knowledge editing.\n\n**On the Generalization of the Method**: The entire method is not overfitted to classification tasks and CNN architectures. Below are core analyses and explanations of our proposed MDA method:\n\n1. Model Disassembly: Our model disassembly method is based on the proposed contribution aggregation and allocation. Contribution refers to the impact of features or parameters on the model's final prediction (lines 97-99 of the paper). Contribution aggregation refers to how contributions flow when multiple input neurons point to a single output neuron, and contribution allocation refers to how contributions flow when a single input neuron points to multiple output neurons (Figure 1 in Section 3.1 and Figure 5 in Appendix C). Based on contribution aggregation and allocation, we can continuously locate features most relevant to the prediction and thus identify the parameters most closely connected to these features (lines 157-162).\n2. Model Reassembly: For disassembled parameters, through padding strategies to match parameter dimensions (lines 208-211) and parameter scaling strategies to align parameter magnitudes (lines 228-232), sub-models from different source models can be assembled and directly used for inference.\n\nAs seen, our proposed model disassembly and assembly method is not strictly bound to CNNs. It can serve as a general paradigm for solving model disassembly and reassembly tasks, with the potential to generalize to other tasks and deep neural networks. Specifically, besides convolutional layers, we also provide methods for contribution aggregation and allocation in linear layers, which are essential for networks like graph neural networks (Appendix C). Furthermore, in Section 5.3, we demonstrate the effectiveness of our method on graph convolutional networks, indicating that the MDA method remains effective.  Additionally, in response to your third comment (Q3), we have included experimental results demonstrating the applicability of our proposed method to Transformer models.\n\n**On Applicability to Self-supervised Learning**: We acknowledge the importance of self-supervised learning but believe it does not limit the applicability of our model disassembly and reassembly method.\n\n1. Self-supervised learning generally involves using pre-tasks like context prediction or contrastive learning to obtain pre-trained models, which are then applied to downstream tasks like classification, detection, segmentation, or question answering in NLP. Given our above discussion on sub-task definitions and method generalization, applying model disassembly and assembly to models based on self-supervised learning is feasible and practical.\n2. Regarding pre-trained models obtained through self-supervised learning, we believe directly applying model disassembly and reassembly to pre-trained models might not be particularly practical since these models are typically used for broad feature learning and cannot be directly applied to specific downstream tasks. However, this does not preclude the applicability of model disassembly and reassembly. For instance, pre-trained models can be decomposed into parameters related to the pre-task and unrelated ones, facilitating model compression by removing parameters unrelated to the pre-task. As shown in Appendix J.2, this approach to model compression has potential benefits.\n\nThank you again for your valuable feedback. Applying model disassembly and reassembly to broader areas, such as NLP, is a key focus of our ongoing and future research efforts.", "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": "", "extra_scores": {}}, {"review_id": "b11L9ivvC1", "reviewer": "Reviewer_gfoc", "summary": "This 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.", "questions": "1. 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?", "limitations": "The limitations have been discussed in the paper. Additionally, there is no societal impact from the work performed.", "rating": 7, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "1. 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.", "weaknesses": "1. 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your review and comments. We are pleased that you find our work on model disassembly and reassembly to be novel and consider it to be inspiring for model interpretability and architectural design. Below are our responses to each of your comments (your comments are highlighted in italics).\n\n> Q1: 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> \n\n Thank you for your comment. We respectfully disagree with this viewpoint. Firstly, there is no evidence suggesting that the performance of disassembling decreases with an increasing number of categories. In fact, as shown in Table 1, regardless of the number of categories into which the model is disassembled, the performance of the disassembled models generally exceeds that of the original model, though the degree of improvement may vary. This is because model disassembly extracts only the parameters relevant to the target categories, reducing interference from unrelated parameters and thereby enhancing prediction accuracy. More importantly, the time required for model disassembly is minimal compared to training a new model, as detailed in our response to the first comment by Reviewer FhUd.\n\n> Q2: *Manually setting thresholds (Eqns. 9 and 10) to obtain relative contributions may not guarantee consistently good results across tasks.*\n> \n\nThank you for your feedback. We agree that manually setting thresholds (Equations 9 and 10) may not consistently yield optimal results across different tasks. The choice of thresholds represents a trade-off between the performance of the disassembled model and the number of model parameters. Consequently, the optimal thresholds for Equations 9 and 10 may vary across different tasks. To address the impact of manually chosen thresholds on the results, we are currently exploring a more automated approach. This involves developing adaptive algorithms that dynamically adjust the thresholds to meet the specific requirements of different tasks. Thank you again for your valuable suggestion.\n\n> Q3: *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> \n\nThank you for your comment. The table you are referring to should be Table 2. We acknowledge that in some scenarios, the performance of assembled models without fine-tuning can be inferior to that of the original models. As discussed on lines 270-272 of the paper, the assembled submodels originate from different source models, and even the data used to train these original models may differ. Consequently, during prediction, the numerous parameters of these submodels can interfere with each other, affecting the accuracy of the assembled model. This is indeed a limitation of our current approach and an area for future work, as mentioned on lines 341-343 of the paper.\n\nHowever, this method still holds significant value. Currently, to address this limitation, we fine-tune the assembled models to mitigate interference among the submodels. It is important to note that this fine-tuning requires only a few iterations (e.g., the fine-tuned results in Table 2 are based on 10 iterations), which is substantially less computationally expensive than training a model from scratch. In some cases, the performance of the final fine-tuned model even exceeds that of the original model. Notably, fine-tuning is not required during the model disassembly phase.\n\n> Q4: *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> \n\nThank you for your detailed comment. We understand your concerns and agree with your observations. Indeed, the failure of certain categories to adhere to their own pathways extracted from the model disassembly can result in degraded performance due to what you described as \"inter-class interference.\" To address this issue, we have implemented the following measures during model assembly:\n\n1. Parameter Scaling Strategy: During model assembly, we apply a parameter scaling strategy to mitigate significant differences in parameter magnitudes between disassembled submodels. This helps prevent the assembled model's predictions from being disproportionately influenced by submodels with larger parameter magnitudes. The results of our ablation studies on this strategy are presented in Table 4.\n2. Minor Fine-Tuning Mechanism: As mentioned in our response to your previous comment, we consider performing a small number of fine-tuning iterations after model assembly. This helps the model adapt to the new data distribution and can enhance overall performance.\n\nAdditionally, we are exploring more generalized and stable methods for assembling submodels to prevent the occurrence of \"inter-class interference.\" Thank you once again for your valuable feedback.", "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": "", "extra_scores": {}}, {"review_id": "2PWeYrSzS1", "reviewer": "Reviewer_17d9", "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.\n\nThis 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.", "questions": "refer to the weakness part.", "limitations": "This paper only design CNN-based method, ignoring the popular Vit model.", "rating": 7, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "The paper is clearly written and easy to follow.\nThe proposed task is very novel, and the whole pipeline is well presented and easy to understand, and makes sense.\nThe locating-then-assembling paradigm is well supported by the informative components identification and alignment padding strategy.\nThe 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?\n\nAlso, in the 4.2, why the Parameter Scaling Strategy can balance the effects of different componens?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your review and comments. We are pleased that you find our work to be novel and that you appreciate the clarity of our proposed pipeline and the convincing nature of the results. Below are our responses to each of your comments (your comments are highlighted in italics).\n\n> Q1: *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?*\n> \n\nWe apologize for the confusion. The \"contribution of features\" refers to the extent to which features impact the prediction results (as described in lines 97 to 99 of the paper). This contribution is dynamic and continuous throughout the network layers during inference (lines 100 to 104). Based on this concept, we introduced two mechanisms for contribution flow: contribution aggregation and contribution allocation.\n\nIt is important to note that contributions are relative, meaning that a larger feature value has a greater relative impact on the model's final prediction (lines 129 to 131). In Sections 3.1.1 and 3.1.2, the contribution of features during aggregation and allocation is defined by their relative magnitude (Equations 5 and 8). Additionally, to account for nonlinear functions such as ReLU, we only use features with positive values to compute this contribution (Equations 4 and 7). Our experiments demonstrate that this method of calculating contributions effectively identifies key parameters related to subtasks.\n\n> Q2: *Also, in the 4.2, why the Parameter Scaling Strategy can balance the effects of different components?*\n> \n\nWe apologize for any confusion. The term \"effects of different components\" refers to the varying magnitudes of parameters from different source models, which can influence the output disproportionately. Even if an input does not belong to a particular task-aware component, if the parameters of this component are of a significantly larger magnitude, it can produce a disproportionately large response, thereby skewing the prediction (lines 228 to 232 of the paper).\n\nThe Parameter Scaling Strategy addresses this issue by scaling the parameters of each task-aware component before assembly. This scaling is based on the response of each component to the same input, ensuring that the magnitudes of the parameters are kept at a consistent level. This prevents any single component from disproportionately affecting the prediction. Ablation experiments presented on lines 316 to 320 of the paper demonstrate the effectiveness of this strategy. We hope this explanation clarifies your concern.\n\n> Q3: Limitations: This paper only designs CNN-based methods, ignoring the popular ViT model.\n> \n\nWe understand your concern.  The proposed MDA method, including the underlying concepts of contribution aggregation and allocation, is indeed applicable to Transformer models. The method is not tightly coupled with CNNs and can be applied to linear layers and other structures beyond convolutional layers (Appendix C), making it a general paradigm for model disassembly and reassembly tasks. This is demonstrated in the experiments presented in Section 5.3 of the paper. Additionally, we conducted a preliminary model disassembly experiment using ViT-Tiny on ImageNet-10, and the results are shown in Table 1.\n\nTable 1: Disassembly performance of ViT-Tiny on ImageNet-10.\n\n| Disassembled Task | Base (%) | Disa (%) |\n| --- | --- | --- |\n| 0-1 | 87.70 | 89.25 (+1.55) |\n| 1-3 | 75.10 | 78.10 (+3.00) |\n| 3-9 | 77.51 | 79.29 (+1.78) |\n\nAs shown in Table 1, the disassembled model can still be used for inference, with accuracy exceeding that of the original model.\n\nAdditionally, we further tested the assembly performance of ViT-Tiny on CIFAR-10 and CIFAR-100. However, the results were not satisfactory. For instance, when assembling disassembled CIFAR-10(0-3) and CIFAR-100(11-17) models, the accuracy without fine-tuning was only 13.27%. The primary reason for this is the task interference that occurs when submodels from different source models are assembled, a phenomenon that is particularly severe due to the self-attention mechanism inherent in Transformers. We believe that adjusting parameter distribution from different submodel parameter spaces before assembly could be a potential solution to this issue, which will be a focus of our future work.", "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": "", "extra_scores": {}}]

Author rebuttal:
Dear Reviewers FhUd, UTXG, gfoc, and 17d9,

Thank you for the time and effort you have invested in reviewing our paper. We are particularly grateful for your recognition of the novelty and insight of our work and are pleased that our proposed model disassembly and reassembly approach has been deemed inspiring for model interpretability and model design.

Through our detailed responses to each of your questions and comments, we believe that the paper has been significantly improved. We have compiled the common concerns raised by the reviewers and have addressed each comment individually. If you find that our responses satisfactorily address your concerns, we would be grateful if you could consider increasing the score. If you have any further questions, we would be more than happy to engage in additional discussions.

> Reviewers FhUd, UTXG, gfoc, and 17d9 all raised questions about why the performance of MDA can even exceed that of the base model.
> 

We apologize for any confusion caused. We have addressed this question in detail in the responses to each reviewer’s comments. In summary, the performance improvement of MDA can be attributed to two main factors: 1.The original models from which the components are disassembled can be considered as having been trained on additional data compared to the baseline model for the target tasks. 2.The disassembled submodels contain only the parameters relevant to the corresponding subtasks, which helps avoid interference from parameters related to other subtasks.

> Reviewers FhUd, UTXG, and 17d9 all raised questions about the applicability of MDA beyond convolutional neural networks.
> 

We have addressed this question in detail in our responses to each reviewer. In summary, we have explained the potential applications of MDA methods to other architectures and tasks, such as classification, segmentation, and large language models. Additionally, we have provided supplementary experiments in the paper and responses demonstrating the effectiveness of MDA in architectures like graph convolutional networks and Transformers.

While model disassembly is applicable to networks such as Transformers, we acknowledge that task interference can occur during model assembly in Transformer architectures. We have provided explanations for this phenomenon and potential solutions. We will continue to explore the applications of model disassembly and assembly in deep learning, with a focus on Transformer models and Transformer-based large language models as a key area of ongoing and future research.

> Reviewer UTXG has focused on issues related to the definition of subtasks in model disassembly and reassembly, the generalizability of the method, the applicability of self-supervised learning, and a systematic comparison with related methods.
> 

Thank you for your comments. We have provided detailed responses to each of these issues under the respective comments. These include explanations on the definition of subtasks related to images and non-images, an overview of the proposed method and its generalizability, how model disassembly and reassembly can be applied to self-supervised learning, and a systematic comparison with other methods. We believe these detailed explanations will help reduce any misunderstandings about our paper.

> Reviewer gfoc emphasized the issue of category or subtask interference during model assembly in several comments.
> 

Thank you for your suggestions. We have provided detailed explanations under the respective comments regarding the causes and consequences of interference between classes or subtasks, the specific measures we have taken to mitigate this interference, and potential future approaches to further address it. We believe these responses will effectively address your concerns.

Beyond the issues mentioned above, more detailed responses to each reviewer’s comments can be found below their respective sections. We sincerely thank the reviewers for their time and valuable feedback, which have greatly contributed to the improvement of the paper.

Meta-review: The submission received three Accept (7) and one Strong Reject (2). Reviewer UTXG raised concerns including complicated method, limiting to classification tasks, lack of systematic comparisons with "Deep model reassembly.", the absence of transformer-based experiments. The AC checked the authors' rebuttal to Reviewer UTXG and was convinced that these concerns are well addressed. All the reviewers agreed the **technical novelty of the paper, including Reviewer UTXG**. Given the strong recommendation from three reviewers and the technical novelty of the paper,  the AC recommends this paper for acceptance.
Decision: Accept (poster)