Style Adaptation and Uncertainty Estimation for Multi-Source Blended-Target Domain Adaptation
Authors: Yuwu Lu
Haoyu Huang
Xue Hu
Venue: NeurIPS 2024 poster (2024)
Keywords: Domain adaptation
Transfer learning
Multi-source and blended-target
Style transfer
Uncertainty estimation
Primary area: machine_vision

Abstract: Blended-target domain adaptation (BTDA), which implicitly mixes multiple sub-target domains into a fine domain, has attracted more attention in recent years. Most previously developed BTDA approaches focus on utilizing a single source domain, which makes it difficult to obtain sufficient feature information for learning domain-invariant representations. Furthermore, different feature distributions derived from different domains may increase the uncertainty of models. To overcome these issues, we propose a style adaptation and uncertainty estimation (SAUE) approach for multi-source blended-target domain adaptation (MBDA). Specifically, we exploit the extra knowledge acquired from the blended-target domain, where a similarity factor is adopted to select more useful target style information for augmenting the source features. \!Then, to mitigate the negative impact of the domain-specific attributes, we devise a function to estimate and mitigate uncertainty in category prediction. Finally, we construct a simple and lightweight adversarial learning strategy for MBDA, effectively aligning multi-source and blended-target domains without the requirements of domain labels of the target domains. Extensive experiments conducted on several challenging DA benchmarks, including the ImageCLEF-DA, Office-Home, VisDA 2017, and DomainNet datasets, demonstrate the superiority of our method over the state-of-the-art (SOTA) approaches.

Style Adaptation and Uncertainty Estimation for Multi-Source Blended-Target Domain Adaptation
Yuwu Lu," Haoyu Huang, and Xue Hu
School of Artificial Intelligence, South China Normal University (luyuwu2008, hyhuang99, hx1430940232)41633 com
Abstract
Blended-targe target domain adaptation (BTDA), which implicitly mixes multiple sub- target domains into fine domain, has attracted more attention in recent years. Most previously developed BTDA approaches focus utilizing a single source domain, which makes it difficult to obtain sufficient feature information for learning domain- invariant representations. Furthermore, different feature distributions derived from different domains may increase the uncertainty of models. To overcome these is- sues, we propose style adaptation and uncertainty estimation (SAUE) approach for multi-source blended-target domain adaptation (MBDA). Specifically, we exploit the extra knowledge acquired from the blended-targe domain, where a similarity factor is adopted to select more useful target style information for augmenting the source features. Then, mitigate the negative impact of the domain domain-specific attributes, we devise a function to estimate and mitigate uncertainty in category pre- diction. Finally, we construct simple and lightweight adversarial learning strategy for MBDA, effectively aligning multi-source and blended-targer domains without the requirements of domain labels of the target domains. Extensive experiments conducted several challenging DA benchmarks, including the ImageCLEF-DA Office-Home, VisDA 2017, and DomainNet datasets, demonstrate the superiority of our method over the sate-of-the-art (SOTA) approaches.
Introduction
Domain adaptation (DA), whose objective is to transfer knowledge from one or more well-labeled source domains a non-labeled target domain, has been studied in recent years [1, 2, 3, 4, 5, 6]. including object classification [1, 2], semantic segmentation [3, 4], and object detection [5]. However, most DA approaches are based on a setting that has single source domain and single target domain [1, 2]. In the real world, unlabeled target domains are usually drawn from different distributions. Therefore, most of the existing single barget-based DA approaches may not be the best answer to address domain shifts in reality.
Fortunately, an increasing number of researchers have focused on the above-mentioned issues, and multi-target domain adaptation (MTDA) [7, 8, 9] has been studied. MTDA aims to learn model that can simultaneously utilize information from single source domain and multiple target domains and then perform well in multiple target domains. For instance, HGAN [10] adopts heterogeneous graph attention network explore the relations among multiple target domain features. In [11], multiple expert models employed the corresponding source-targe domain mair-groups and were then aligned by a student model. Although the existing MTDA approaches have made certain progress, the standard MTDA is still facing challenges because massive amounts of unlabeled data drawn from different distributions are commonly used in real-world settings. is time-consuming and expensive to divide massive data into multiple corresponding distributions (target domains). For example, for
"coreepponiig author
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Table 1: Comparison about different settings of DA.
Settings
Source domain number
Target domain number
Domain labels
UDA/SSDA
single
single
MSDA
multiple
single
MTDA
single
multiple
BTDA
single
multiple
MMDA
multiple
multiple
MBDA
multiple
multiple
enerypted data stored in a cloud server, due to privacy protection, models cannot directly know the origins of these data (domain labels), which are drawn from different distributions and are blended into a large target domain. Based on the above-mentioned case, blended target DA (BTDA), which is a more beneficial scenario in real-world settings, has been proposed [12].
Current BTDA approaches [12, 13] are mainly based on three points: 1) the adaptation process contains single source domain and multiple target domains. 2) During the adaptation process, the model cannot access both the domain labels and category labels of the target domains. 3) In blended- target domain, the category labels of each sub-target domain may not follow the same distribution. Therefore, simply utilizing MTDA or SSDA (single source domain adaptation) methods to handle the BTDA task may lead to negative transfer because the domain labels of target domains are unseen, and the category feature space is hybrid space. As the first deep learning work focused on the BTDA scenario, AMEAN [12] employs two adversarial learning steps and utilizes meta-learning to minimize the domain gap between the source domain and the blended-targe domain. However, insufficient information obtained from single source domain makes models difficult align the distributions of multiple target domains. Moreover, the presence of different distributions in the blended- target domain may aggravate negative transfer. Recently, multi- muuti-source domain adaptation (MSDA) [14, 15, 16] has produced impressive results. MSDA approaches can utilize more feature information from extra source domains to learn domain-invariant representations, effectively solving negative transfer. However, as far as we know, no related works have been proposed to utilize more feature information from multiple source domains for BTDA.
In this paper, to further exploit feature information from multiple domains, we pay attention to the BTDA in the case of multiple source domains, i.e., Multi-Source Blended- Target Domain Adaptation (MBDA). The comparisons of different DA settings are illustrated in Table 1. At the same time, style adaptation and uncertainty estimation (SAUE) method proposed for MBDA. Different from previous works, we utilize the style information of the blended-targe domain to enhance source domain features, thus building a better representation space. Specifically, we first simultaneously extract the source and target style information and then calculate the similarity factors between the source and target style information. The similarity factors are served as the weighted matrix during the feature augmentation process. Based style adaptation, the classification model and adopt a loss function to eliminate the uncertainty introduced by the multi-source domains. In addition, the domain labels of the cenneed-targe domain are unavailable in MBDA, we construct an adversarial learning scheme for MBDA without the requirement of domain labels of the target domains.
further analyze the uncertainty in
The main contributions of this work are summarized as follows:
An approach SAUE is proposed to explore information from multiple source domains for BTDA. As far as we know, SAUE is the first work that was proposed for MBDA, which utilize more feature information from extra source domains to learn Nomain-invarian representations.
To further exploit the style information in the blended- target domain, we propose a similarity- based style adaptation strategy for MBDA, which selects target styles to enhance the source representation space.
We propose an uncertainty estimation technique to enhance the robustness of our method, which exploits valuable knowledge from multiple source domains. In addition, we construct a specific adversarial learning strategy for MBDA, which aligns domains without the requirement of domain labels.

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2 Related Works
Single-source and Single-target DA (SSDA). The objective of SSDA is to learn
representations through the relations between the source and target domains. Based on this objective, researchers have carried out widespread researches and achieved certain progress [17, 18, 19, 20, 21, 22, 23J. The current SSDA methods are mainly divided into two types: distance metric-based approaches [17, 18, 19] and adversarial learning-based approaches [20, 21, 22, 233. Distance metric- based methods learn domain-invarian representations through feature discrepancy matching by using distance metric function. DAN [18] utilizes multi-kernel maximum mean discrepancy (MMD) to measure the discrepancy between the source and target domains and then minimizes the discrepancy to learn domain-invarianl
representations. CAF [19] utilizes sliced Wasserstein distance (SWD) [24] to measure domain discrepancy. Motivated by the GANs [25, 26]. adversarial learning- based SSDA methods have also been widely researched [20, 21, 22, 23). Adversarial learning methods perform min-max two-player games between the category classifier and domain discriminator to learn nomain-invarian representations. DANN [20], which was the earliest work in adversarial learning-based SSDA, successfully achieves domain-level adaptation via a gradient reverse layer. Different from DANN, SCDA [22] and DALN [23] remove the discriminator from their networks and model the adversarial relation between the feature extractor and category classifier. Although the menoeeeent approaches have achieved great success, due to the limitations of single source domain features and single target domain features, current SSDA methods still face some challenges in real applications.
Multiple Domains DA. The motivation of multiple domains DA is to explore more useful knowledge from multiple domains for the tasks. Many researchers have focused on multiple-domain DA and proposed many excellent methods [13, 14, 15, 8], including MSDA [14, 15, 27, 28], MTDA [8], BTDA [13. 12], and MMDA (multi-source and multi-target DA) [29]. M3SDA [14] utilizes moment matching to align domain distribution. DCA [15] further extracts the multiview features from multiple source domains and then utilizes multiple classifiers and pseudo-label learning strategy to align distributions. Meanwhile, in MTDA, CGCT [8] utilizes feature aggregation and curriculum learning to learn the pscudo-labels of multiple target domains. AMDA [29] constructs multiple domain discriminators and utilizes attention mechanism to address the MMDA issue.
Recently, a more realistic DA scenario, BTDA, has been studied [12, 13]. For example, MCDA [13] utilizes the mutual condition to learn Nomain-invariane representations, which has achieved great progress in BTDA. However, single source domain in BTDA cannot provide sufficient feature infor- mation for aligning the source and blended- target domains. Furthermore, the unseen domain labels of the target domains also aggravate the challenges. Therefore, we consider multiple source domains in BTDA and utilize the style information of the target domains to optimize the representations of source features and minimize the model uncertainty, thereby obtaining a better transfer.
3 Method
3.1 Preliminary
In MBDA, have M labeled source domains S {S,m J.M | that are drawn from distributions (Ps.. JM 1 Sm = {-;""m y,. )aan) where _.S.. E Rd denotes the i-th source sample from the m-th source domain and y is the corresponding category label, and d denotes the number of dimensions. Meanwhile, we have an unlabeled seende-ttagget domain T that consists of N sub-target
domains (Ta J and T = Therefore, the distribution of blended- -taggtt domain Py is the mixture of sub-target domains, i.e., Py = -== PT. where E [0, 1] and Σ nr = 1. Each of the source and target domains shares the same category space. The objective of MBDA to train model that utilizes multiple source domain features and performs well on the plended-target domain. Different from MTDA, the target domain labels are unseen in MBDA. In addition, the analysis in [12] demonstrated that directly utilizing DA methods to address BTDA transfer tasks may cause increased uncertainty and negative transfer. Therefore, we utilize the style information of the blended ttrggt domain to augment source features and minimize the uncertainty of the model. Furthermore, the adversarial learning strategy in our method without the requirement of domain labels of the sub-target domains suitable for MBDA setting. Figure I illustrates the overall architecture of SAUE.
The distributions of sub-target domains are {PT, J
3

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Dutuflow
Dauflow
Bickgeopaation
Cendiert Layer
DiA (2 D(n 16.--
$1
fisss
0f
Siee
STReT
Wenerath Disbaees
Stmilarity Factere A
lllended Derger
Lyne
|| I-llx
Maps
I|-1NN
GRI
Eyord
Convoolution Layer
Style Adaptation
Convelution Layer
Featuure Extractor with Style Adaptation
Classifier and Uneettainty Estimation
Figure 1: Overview of the proposed SAUE approach. First, the style information of the blended-targed
domain is utilized to augment the source features through the similarity factors. Second, we calculate the uncertainty of the model and optimize prediction uncertainty via the Dirichlet distribution. Finally, the adversarial learning strategy without discriminator effectively guides the SAUE process adapt the blended-targe target domain without the requirement of domain labels of sub-target domains.
3.2 Style Adaptation from Blended-Targee -Trgett Domain
Since the principal parts of features from different domains remain domain-invariant, the domain- specific parts, which mainly contain style information, are the main discrepancies between different domains. addition, the target feature distributions in MBDA are confused, which may cause model degradation. Therefore, we try to utilize the style information of blended-target domain to augment source features, which brings source features closer to target features. Previous work [13] has demonstrated that low-level features of deep neural networks (DNNs) mainly represent style information. Some works [13, 30] have utilized the channel-wise mean and variance of the low-level features to represent the style information of input samples. Thus, for sample Sm. which from the m-th source domain, let its low-level feature be 2:Sh E ss. xWsm, where d denotes the channel and Hs,,, Ws... denote the height and width of sample x;." The channel-wise mean and variance of the lewelevel feature zi can be defined as follows:
Hsm
Ws
Hsm.
Wam
Sm
p4
HS.. W WSm.
Hs., Wsm..
Low-level features mainly represent style information, but different samples contain specific pieces of style information. Therefore, we adopt feature normalization technique to standardize the feature Sm and the normalized feature 3Sm is defined as:
28m
Sm
+
where is a small value used to avoid division by zero.
Then, the target features will be utilized to augment the normalized source features. Previous work [13] randomly augmented source features through target style information, which yielded limited performance. In our work, we select the target style information according to weight factor. Specificallly, we leverage the Wasserstein Distance [3 to measure the style distribution discrepancy wSSn between the source low-level feature 2. and the target low-level feature 2] as follows:
w
13 |l (o. (o))² 20 mm
(3)
Then, utilize ff to calculate the weight factor follows:
exp(1/(1 +w w
(4)
m= =1 cxp(1/(1 + w

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To ensure that the sum in Eq. (4) equals to 1, we utilize softmax operation to normalize each AST.. Then, can obtain the weighted target style as follows:
M
M Σ
Sml
H4
0
1m X
0
(5)
Finally, the low-level source feature augmented by the blended-target style information can be calculated follows:
T
(6)
Different from previously developed image augmentation method [13]. our method directly utilizes target information with weight factor to augment source features instead of generating specific images. The low-level feature S... augmented by diverse target styles further mitigates the impact of the domain-specific attributes.
3.3 Uncertainty Estimation and Elimination
Although multiple source domains provide additional supervised information for adaptation compared single source domain, they also introduce domain-specifie attributes. This cause model degradation, especially when some source domains are significantly dissimilar to the blended-targes domain due the abundance of domain-speeifee attributes. Evidential model learning (EDL) [32] is an interpretable approach that fuses knowledge from multiple domains using the Dempster- Shafer Rule [33, 34], which is more beneficial MBDA scenario. Thus, we utilize the Dirichlet- evidential model [32] to estimate the uncertainty of our model during the training process. Specifically, for sample I,, we have the predictions p; C(G(x;)) = [pt1. Piz..... ... the classifier and feature generator, respectively. The probability density function of is defined as follows:
Pix .......PP...
where C and G denote
ai--1 for U D(pila.) = Blo.J 1==1 Pk
0
otherwise
where UR = (pel E Pik 1 and S Pil, ..., PiK S 1} is the K-dimensional unit simplex r as)) is the K-dimensional and a; is the parameter of the Dirichlet distribution. B(a;) 11 [(ow) multinomial beta function, and (-) denotes the gamma function.
Previous work [32] has demonstrated that DNNs can generate opinions for classification tasks as Dirichlet distributions. Therefore, given sample xú, for prediction of class e that generated by DNNs, the Dirichlet distributions connected with DNNs can be defined follows:
(G(()))
Ply =c(a;) En=r 0sk
EK (x(((z)) R[D(pi/as)]-
(8)
k==
The derivation of Eq. (8) is provided in Appendix B.
In this work, for source sample -.f ; we utilize the prediction of the category classifier as the evidence vector, and the parameters of the corresponding Dirichlet distribution can be defined as a,Sm C(G(xSm) + To eliminate the uncertainty, we force the total evidence zero when the model generates an incorrect prediction for the source sample, and the corresponding uniform Dirichlet distribution is
1)). For implementation. we utilize the Kullback-Leible (KL) divergence to reduce the impact of incorrectly classified source samples in our loss function, which is defined as follows:
Luno
KLIDW, Sm la.S MIDDR (1, (I....., 1})],
where āg Sm (1 evidence from prediction a Specifically, the KL divergence is:
a denotes the Dirichlet parameter used remove the true
KL[D(p,
(p
(1,...,1))
r
1- aFSmm) I(āsk).
(10)
= log
(a. - 1) ((ā)) /
aig)
[(K)
#=1
where I'(-) and (-) denotes the gamma function and digamma function, respectively.
5

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Algorithm SAUE for MBDA
Input: Source domains {Sm |m=1 and the corresponding data {-."". vv." ". blended-targel
!
domain data {x]] }. hyper-parameters X and As maximum iteration /, and mini-batch size
B.
Output: Optimal feature generator G and category classifier C. for in 1:/ do
2:
Randomly sample mini-batch of B source samples and target samples.
3:
Augment the source features by utilizing style adaptation, i.e., Eq. (6).
4:
Minimize the parameters of the feature generator and category classifier by Lels. Optimize the uncertainty of model through Lunc-
5:
6:
Perform the min-max game between feature generator and classifier with Ca:
min max La(ss x
3.4 Domain Adversarial Alignment without Domain Labels
Existing works [20, 21, 22, 8] have demonstrated that adversarial learning strategy is beneficial in DA. However, most of the adversarial learning strategies in DA [20, 21] usually request the domain labels of the source and target domains to train their discriminators, which cannot satisfy MBDA. Inspired by the intra- and inter-class correlation [35], we design an adversarial learning strategy for MBDA without discriminator and domain label requirements. Specifically, the category classifier is reused discriminate which domain feature originates from, with the guidance of the Nuclear norm |1-Il-- We first measure the distribution difference between the source and blended-taid -tagget domains through the nuclear-norm I-Wasserstein discrepancy (NWD) [23] and then utilize gradient reverse layer (GRL) [20] to maximize the discriminative loss of the classifier, Simultaneously, we minimize the feature generator to play the min-max game with the classifier through the NWD, First, the NWD loss can be defined as:
Lm xT)
1
Σ
((G((T))
1 ITi Σ ((GG((
(11)
S
Then, the adversarial learning strategy between feature generator and classifier is defined as follows:
min max La(xS" x
(12)
3.5 Model Optimization and Theoretical Analysis
Overall Objective. The overall loss function that optimizes SAUE for MBDA is defined as:
min G.C
{
Lets (w xx) de (u Xa max Ca(x (,x)xx)))) )T
(13)
where A = min elx)) E [0, is the annealing coefficient, which prevents Lune from over- penalizing the neural network uniform distribution in the early training epochs, and is the current number of epochs and X 40. Xa is hyper--praatne parameter which is initially set to Y ===1 1 as in [23]. Lote is the classification loss, which ensures that the category classifier can correctly classify samples. With the the cross- cross-entropy loss Lee. the classification loss Lass is defined as follows:
Smi Loe (C(G(())) "" S
Sm Sm sm
(14)
After adversarial training, our model can effectively adapt the blended-target domain without the requirement of domain labels of sub-target domains. The concrete steps of SAUE are shown in Algorithm 1.
Theoretical Analysis. Here, we utilize PAC-Bayesian theory [36] to optimize our classification model with uncertainty estimation and elimination. The full-bound theorem motivated by previous work [37] is illustrated in Theorem I and Lemma 1, and the proofs are provided in Appendix C.

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Theorem 1 [37]. Suppose we have given the m-th source data distribution Ps... a hypothesis set H. and prior distribution π over the hypothesis space Q. For any (0, 1] and X O, with probability at least T over the source samples Sm ~ Ps all posteriors p. we have:
1 X [L(pllT) + +og 1 + v ( Ep(H)(C(H)) - BA Ls.. (H)) +
(15)
where V Smi. (2, log (1t) S. ((CECH)-CCH))
Lemma [38]. The PAC-Bayes bound, involving constants and n, as introduced in Theorem 1, is minimized by the Bayesian posterior p(H), which represents the distribution over θ.
During uncertainty estimation and elimination, just as in Eq. (9), we utilize D(p (ā ) as the posterior distribution and D(p." (1....,1) 1)) as the prior distribution. Therefore, the upper bound of the classification model can be expressed
M
1Sm
Σ
Lds +
1 "I(D(p,""
"a "DD(E" (1, 171)
(16)
Sm
Generalization Bound. In this part, prove why SAUE performs well the blended-targer domain via Lemma 2 and Theorem 2. The proofs and derivations are provided in Appendix D.
Lemma [39]. Suppose we have given the probability measures VS.. VT E P(F) of the m-th source feature fs.. and the blended-targ domain feature fT. a hypothesis space Q and a subspace H. E O. Let JF denote a fixed representation space and c(fs.. fT) denote the adaptation cost. For the ideal classifier h H and any classifier h H with fsm. VS... and fr VT, have:
Iks.. (h, - (h, S 2 dHAH(US... VT):
(17)
where cs.. and eT denote the error on the m-th source domain and the error on the hended-targer domain respectively, and ET = N E}=1 duAH denotes the HAH H-distanacc
Theorem 2. Based on Lemma 2, with the error of the ideal joint hypothesis n' cs.. (h') + +T((h') which is a sufficiently small constant, for any E (0, 1), with probility at least 8, for every E H. ey (h) is bounded by the following terms:
(T) ESm. (h) (
1 ddxxilss PT) + VV
2d (olog(2))) (2b') ) + log(3)
n'
(18)
b'
where of = es,, (1²) + (h') is the ideal error for the classifier, which is a sufficiently small constant. is the size of unlabeled samples.
Therefore, the final objective of the MBDA classification task is reduce the joint domain discrepancy term m=1 les... (h, h²) ET (h, h()l.
M
4 Experiments
4.1 Datasets and Implementation Details
Datasets. Four standard benchmark datasets are used validate the effectiveness of our proposed method. The ImageCLEF-DA [40] contains 2,400 images and is divided into 4 domains: Bing (b), Caltech (c), ImageNet ()) and Pascal (p). Each domain has 12 categories, and every category has 50 images. The Office-Home [41] also consists of domains and 15,588 images belonging to 65 categories from four subdomains: Art (Ar), Clipart (CI), Products (Pr), and Real world (Rw). The DomainNet [14] is large-scale dataset in DA that contains 0.6 million images of 345 categories from 6 domains: Clipart (C), Infograph (I), Painting (P), Quickdraw (Q), Real world (R), and Sketch (S). Following the protocol used in [29], we select 126 categories and 4 domains (C, P. R, and S) in our experiments. The VisDA 2017 [42] dataset a challenging dataset consists of domains (Syn. and ReL.) and categorics.
7

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Implementation Details. We utilize PyTorch framework [43] to perform our experiments; the PyTorch version is 1.13.1 and CUDA version is 11.7. We use an ImageNet pre-trained ResNet [44], replacing the last FC layer with task-specific FC layers. All experiments are run single GeForce RTXX-4990 GPU, and the batch size of both the source and blended-target domains are set to 32. The optimizer is Stochastic Gradient Descent (SGD) with a momentum parameter of 0.9 and a weight decay of le-3. The learning rate is 1e-3 and updated by the LambdaLR [43] during the training process.
4.2 Comparisons to State-of-the-Art
To evaluate the effectiveness of our proposed method, we conduct extensive experiments and compare our approach with the state-of-the-ar (SOTA) methods in terms of DA classification. The comparison methods include SSDA approaches, i.e., MCD [45], DAN [18], TSA [46], DALN [23], BIWAA [47], and SCDA [22]; MSDA methods, iee, MDAN [48], DCTN [49], and DIDA [50]. MTDA/BTDA methods: MTDA-ITA [7] and MCDA [13]; and Multi-source Multi-target DA (MMDA), i.e., AMDA [29] and HTA [51]. The comparison results are presented in Tables 2-4, in which we select two domains as source domains and combine other two domains to form the blended-target domain. Note that these approaches do not totally match the MBDA setting. Therefore, we utilize the following rule for our comparison. For SSDA setting, one column denotes one SSDA task, such R-C in Table 2. For MSDA methods that contain more than two source domains, we implement those methods according to their released codes, reset the source domain into two domains, such as R+S-C, and mark them with "*" Similarly, under the MTDA and BTDA settings, we reset the target into two domains and select the highest one in MTDA/BTDA task group that contains the same target domains, such as R C+P and S-> C+P in Table 2. For MMDA setting, two domains are sources, and the other domains are targets, such as 2-4 are the averages of two target domains.
in Table 2. For better comparison, all results in Tables
Results on the DomainNet are displayed in Table 2. Our SAUE method achieves SOTA performance in most of the experimen- tal groups and attains the best performance in terms of average accuracy. Compared to the BTDA method MCDA in multi multi-source set- ting, our method achieves better performance because the style information of the target do- main selected by the weight factor can en- hance the source feature representations. Com- pared to MMDA method AMDA, although AMDA can access the domain labels of the target domains, our method still overpasses AMDA in terms of average classification accuracy (overpass 7.5%) and without the requirement of the domain labels of the target domains. Furthermore, both AMDA and our method are adversarial learning methods, and the comparison results further demonstrate the effectiveness of our adversarial learning strategy. These obtained improvements are mainly due to the uncertainty optimization process and the style information derived from target features.
Table 2: Accuracy (%) on the DomainNet for MBDA (ResNet-50).
Method
R+5
S4P
P4R
C4S
RAC
C+P
Avg.
C+R
C45
DANN IMLR'
31.4
39.7
20
31.6
DANUS)] TRAMT
32.8
40.6
29.8
31.5
32.5
MDAN[48] NewtPS 18
54.5
59.0
45.0
58.8
51.7
61.0
54.5
MIDA TIP
52.4
48.7
45.5
53.3
51.5
52.0
50.5
AMDA[29] TIP
58,9
63.4
DAL CVPR222
61.2
69.2
63.5
59.3
63.7
MCDA- AAAT25
60.2
68.7
61.7
63.4
61.2
65.4
63.8
DGWA [122] TMM224
66.4
71,3
63.4
67.5
64.6
70.2
65.0
SAUE (Ours)
70.8
76.9
67.6
71.9
65.2
73.1
70.9
denotes that the results are obcained by: the released code of the conrespondngg method. The best results are boldad.
Table 3: Accuracy (%) on the (a) Hhmee--Hmeeeee and the (b) ImageCLEF-DA for MBDA (ResNet-50).
(1) OfficeHooee
Imsp:CLEF-DA
Method
Ru-Pr Cli-w Pt-CI Rw-Ar AFFCI
Ar-Fi
Ar-Rw
CIFFi
Ar-Pr CI+Rw
PF+R Avg.
Cl+Ar
Method
1tp
psc
bap
ctp
1-h
h4c
Avg.
DANN IMLR'16
53.5
53.5
55.6
60.1
$7.6
DANN IMI R116
76.4
72.4
69.11
87.9
62.9
79.3
77.9
DAN TRAMI19
53.4
60.11
52.2
54.3
58.7
56.3
DAN 19AM
78.3
74.8
91.5
78.8
79.8
MTDAIT] TIP
51.9
64.9
60.3
59.4
582
62.4
59.5
CDAN*(211 NeurlPS'
78.
76.8
68.2
92.8
85.9
82.5
80.6
MDANI (8) NeurtS 18
55.4
69.1
61.2
61.5
55.9
70.4
62.2
AMDA
29| TIP
78.8
27.3
71.7
92.3
$5.2
83.8
81.5
SCDA CVPR'21
61.1
74.7
70.0
68.3
68.7
77.6
70.]
SCDA* CVPR
78.9
77.2
71.8
93.9
85.5
85.0
82.1
AMDA[29] TIP 21
61.4
77.0
72.3
67.4
64.9
77.4
70.0
DIDA [10) TIP 22
78.9
77.9
72.0
92.2
86.8
85.1
82.2
HTA[S1] Appl. Treell
62.2
78.9
75.0
68.7
66.2
79.0
71.9
HTA[S]] Appl. Intell
79.3
78.2
72.3
92.8
85.6
84.9
82.2
MCDA "1 AAAI22
63.6
70.0
78.1
70.6
MCDA AAAU23
77.4
79.1
91.8
81.4
DGWAI52] TMM'24
63.7
73.9
70.7
66.9
78.8
DGWA[52] T4 24
79.7
79.1
93.8
82.7
SAUF (Ours)
65.6
79.9
75.2
70.1
71.8
79.3
73.7
SAUE (Ours)
80.8
80.2
94.9
87.2
84.3
denotes that the resolts are obtained by the relessed code of the conesponding method. The best results are holded.
Results on the Office-Home are shown in Table 3a. The experimental results are compared with those of the SOTA methods, illustrating that our proposed method achieves dramatic improvements in most comparison groups and achieves the highest average classification accuracy (73.7%). Note that the Rw domain contains total of 34,856 images, which is far more numerous than the other
8

---

Table 4: Accuracy (%) on the (a) default version of DomainNet dataset and (b) VisDA-2017 dataset (ResNet-101).
VisDA-2017
Method
C+P+Q
R+5
C+P+R
C+P+S
Q4R
C+Q+R
145
C+O+S
C+R+S
P4Q
P+O+R
C95
P+Q+S
P+R-5
Q+R+S
Arg.
Method
Syn. Rel.
50.3
562
30.4
DGWA
54.7
40.7
52.
56.8
30.7
55.3
36.2
57.5
474
SAUE (Ours)
57.7
34.6
42,9
54.7
59.2
36.9
56.0
63.6
37.1
58.7
50.2
Method
C4190
R48
CRTFR
045
C-745
Q-R
CRQHK
P45
C4QFS
C-RPS
P-Q
P4QHK
C45
PRQES
P4K-3
C+Q
Q-R4S
Avg.
SCDA
TSA
MCDA
40.0
59.6
90.2
33.4
41.2
52.9
45.3
40.2
48.3
41.2
64.7
DALN
80.6
SCDA
DGWA
41.2
54.7
50.2
34.3
33.8
41.3
54.2
54.7
46.5
39.3
49.4
42.1
42.6
45.4
45.8
DGWA
803
(Ours)
SAUE(ours)
81.5
three domains. Therefore, the adaptation task faces larger domain shifts and extremely unbalanced classes. The proposed method still achieves 2.8% improvements over AMDA and achieves dramatic improvements in the Rw+Pr-Ar-+h and Rw+Ar tasks. These results occur because the proposed method decreases the impact of unbalanced classes by enhancing the feature representations and optimizing the prediction uncertainty.
Results the ImageCLEF-DA proposed method achieves an average accuracy of 84.3%. 3,,% outperforming the existing approaches. Note that all four domains in ImageCLEF-DA contain 600 images. Therefore, the experimental results further demonstrate that our proposed method is effective when all the domains contain the same samples and classes.
provided in Table 3b. Compared with the SOTA methods,
Results on the Default Version of DomainNet and VisDA 2017. To evaluate the effectiveness of SAUE in different numbers of the source and target domains. We perform comparisons the default version of the DomainNet dataset and the VisDA 2017 dataset, respectively. The default version of the DomainNet dataset consists of 5 domains, leading the division of transfer tasks for MBDA into two categories: C+P+Q-+R RR+S and R+S CC+++. As shown in Table 4, SAUE outperforms the comparison methods across all transfer tasks, achieving the highest average classification accuracy. These results from large-scale datasets further demonstrate the superiority and flexibility of SAUE.
4.3 Experiment Analysis
37
05
246
238
70
40
15
1.5
(a) b4i
(b) h4i
Figure 2: The analysis the SAUE parameters.
Choices
=0.1
A
A
Choices
Aa
A₁
A, =10
96.5
96.7
96.8
96.6
96.5
A- =10
77.5
77.6
78.1
77.9
77.4
=20
96.8
97.0
97.1
97.0
96.9
A. =20
77.7
78.1
78.2
78.0
77.9
A,
97.0
A.
78.3
X, =60
94.7
94.9
95.2
95.1
94.9
A- =60
T1.7
71.9
78.0
77.8
75.9
X, =80
88.7
93.3
93.1
93.2
89.8
A, =80
76.2
77.3
77.9
76.2
74,8
(a) b+i +
(b) b+i +
Table 5: The detailed numerical results corresponding to the relevant tasks.
Sensitivity Analysis. We evaluate the model's performance under different hyperparameter choices. Note that the hyperparameters in our method are the adversarial learning balance parameter A and annealing parameter Ae- As shown in Figure we test different parameter groups to analyze the pa-
rameter sensitivity of our method, where Xa = {0.1, 1,.1... 0.5, 1.0, 1.5, 2.0) and X. = (10, 20, 40, 60, 80). The corresponding numerical results of Figure 2 are illustrated in Table 5. Figure 2, our method
not sensitive to Zg and the best parameter choice is Xa = 1.0, but very sensitive to x (with X 40

---

working best); if be too small, the model will suffer from tremendous degradation due to the model is over-penalized by uncertainty loss.
Ablation Study. As listed in Table 6, we con-
Table 6: Ablation study of SAUE on the Domain- Net.
duct ablation experiments to demonstrate the effectiveness of the style adaptation module and the loss function of the uncertainty opti-
Source Target
R+S CaP
S+P C+R
P-R C+S
C-S P+R
R+C P45
C+P R+5
Arg.
mization process. We test three experimental groups in the DomainNet dataset, including that: 1) remove the style adaptation (SA) mod- ule, 2) remove the uncertainty loss Lunc. and 3) remove both of the bboove-mentioned items.
wlo SA
68.1
72.1
61.2
70.0
60.8
70.1
67.6
w/o Cune
69.7
75.0
65.8
71,2
64.8
72.6
69.9
both
63.8
60.3
wil WD
70.2
77.0
66.3
71.6
61.1
71.5
70.1
SAUE
70.8
76.9
67.6
71,9
65.2
73.1
70.9
The results illustrate that both style adaptation and prediction uncertainty optimization are useful for MBDA. Furthermore, we explore different style adaptation techniques. We directly change the Wasserstein Distance (WD) to randomly selected style information and report the obtained results in the fourth row of middle part of Table 6. Compared with the random augmentation methods, our proposed method can better select the style information through weight factors, which enhances the feature representations of the source domains and reduces the impact of domain shifts. More experiment analysis is provided in Appendix E.
Table 7: Comparison about the SA module on the DomainNet dataset with different backbones.
Setting
C+P+Q R+S
C+P+R Q45
C+P4S
Q+R
C+Q+R
P+S
C+Q+S
P+R
C+R+S
P+Q
P+Q+R
C+S
P+Q+S
C+R
P+R+S
C+Q
Q+R-S
C+P
Avg.
without SA (ResNet-50) 50)
$1.2
28.6
36.7
49.6
52.9
30.2
51.0
57.8
30.3
51.1
43.9
with SA (ResNet-50)
53.9
30.7
39.8
51.3
55.7
33.7
52.8
60.4
33.7
55.3
46.7 (+2.8)
without SA (ResNet-101)
56.1
33.7
41.3
53.1
57.3
35.1
54.8
61.2
364
56.7
48.6
with SA (Re)Net-101)
57.7
34.6
42,9
54.7
59.2
36.9
56.0
63.6
37.1
58.7
50.2 (+1.6)
Setting
C-PFQ
R48
CAPRR
Q48
C4P4S
Q+R
C+Q+R
P&8
C+Q+S
PtR
C+R4S
P+Q
P+Q+R
Cv8
P+Q=S
C+R
P4R+S
CaQ
Q+R-S
C+P
Arg.
without SA (ResNct-50)
39,5
53.2
47,3
33.2
40.1
52.4
45.8
38.2
45.3
40.5
43.5
with (ResNet-50)
41.1
55.3
49.8
35.6
42.5
54.7
39.5
47.7
45.8 (42.3)
without SA (ResNet-111)
42.1
56.5
$1.6
35.2
43.8
$7.0
49.4
39.9
50.7
43.1
46.9
(ReaNet-101)
43.3
57.7
53.1
37.7
41.2
48.2 (+1.3)
Effectiveness of the SA Module with Different Backbones. We have analyzed the performance of our method using the ResNet-50 and ResNet- 101 backbones in the default version of the DomainNet dataset in Table 7. The experimental results show that our method achieves significant performance gains when utilizing powerful backbones (ResNet-101). We compared the performance gains of the style adaptation module with standard backbone (ResNet-50) and powerful backbone (ResNet-100 101). The results indicate that the performance gains (+2.8% and +2.3%) of the style adaptation module when integrated into the ResNet-50 backbone surpass those (+1.6% and +1.3%) when integrated into the ResNet-101 101 backbone.
Effectiveness of the SAUE with ResNet and ViT backbones. We further evaluate the model's performance using different back- bones, as shown in Table 8. When using ViT as the backbone, SAUE outperforms its per- formance with the ResNet-50 backbone. This performance gain is primarily due to the larger number of tunable parameters in ViT-B/16, demon- strating that our method effectively leverages these additional parameters to exploit transferable knowledge from multiple domains.
Table 8: Comparison about different backbones on the Office-Home dataset.
Method
Ru+Pr Amtll
Cl+Rw
Arpr
ArtRw
Pr
Ru-Ar ClePr
Ac+Pr Citkw
C++Ar PrrRe
Avg.
ResNes-50
65.6
79.9
75.2
70.1
71.8
79.3
7377
VIT-B/16
69.2
79.1
5 Conclusion
In this paper. we propose a SAUE approach for MBDA, which utilizes information from multiple source domains to adapt a blended-targed domain. In particular, the style adaptation process utilizes similarity factors to select target style information to enhance the representations of the source features. The uncertainty estimation procedure utilizes the Dirichlet distribution to estimate the uncertainty of the model and then adopts the KI. divergence measure to optimize the prediction uncertainty. The discriminator-free adversarial learning strategy is beneficial for MBDA. Extensive experimental results demonstrate the superior performance of SAUE to that of the competing methods.
10

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Acknowledgment
This work was supported by the National Natural Science Foundation of China (Grant No. 62176162) and the Guangdong Basic and Applied Basic Research Foundation (2023A1515012885, 2022A1515140099.
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Appendix Contents
This supplementary material provides more details that not presented in the main paper due to space limitations. The organization as follows:
A Broader Impacts & Limitations
B provides the derivations of P dl,) B[D(p::lo)))
provides the proof and derivations Theorem
D provides the proof and derivations of the generalization bound.
provides additional experiment analysis the ImageCLEF-DA dataset.
A Broader Impacts & Limitations
Our work focuses the problem of multi-source flended-targe' domain adaptation (MBDA), which aims to train a model that generalizes well on an unlabeled and distributionnconfusse target domain by leveraging multiple labeled source domains. The effectiveness of our method several real- world datasets suggests that it can benefit relevant applications and communities dealing with domain shifts, such as encrypted data analysis, medical imaging, and autonomous driving. Nevertheless, should also be cautious about potential failures of our method when encountering more significant distribution shifts, such as increased number of source or target domains. In the future, we plan to incorporate more sub-domains into our experiments for further verifying the performance of our method.
B Derivations of P = clæx;) = E(D(piclas))
Given sample z;, for prediction of class that generated by DNNs can be calculated
Ply clz;;)
/ ply = clp:)o(p(lm:)dp;
i Pic p(pelors)dp
(I)
/ J..... Pic plpn Pie....
dppddad pr: dpik
/ Pie p(Piclea)dpie
Pakl and Pie is the c-th element of p.. Then, given p(ps(z;) ~ where p; ((G() = [Pa.,P2, Prz. Pak]
D(pslas), we have p(piclx;)) Beta(piclans Orie Ond Ose), where O: Dk=1 Oak. Thus, further have:
K
p(piclos) B B(asey 040 ase)
Pic aic (1
Pie ) aam-aa
(2)
where B(.,) is the K-dimensional multinomial beta function, and B(aie 040 - asd) Maa -ose) T(.) denotes the gamma function. Based on this, we can further obtain the following
T(as+aa0 a.e)
14

---

derivation:
Ply cxa)) / Pic ((Psela;)dpic
= | Pie' B(ase, 040 ase)
Pie
Pre
(1 Pic)
) -ai -1
dpic
B(ase + 1, - -ase)
Blaio, Q40 0ii))
Oio (1 Pro) jai as dpsc
B(a + 1,000 - aie
(3)
Blaic 1, Oiie)
Blate 040 - aii))
P(ase + 1)T()
T(aso + (asc)
and (aie((i)))(as))
Obe
Ca(G(z)) Σ k= Cx(G(z)))
assT (aa))
Σ
k=
= =[[D(p:ala:))
In our work, the outputs of classifier are adopting the exponential function. Thus, following [53], the outputs of DNNs in SAUE can be viewed as the expectation of Dirichlet distribution.
C Proof of Theorem 1
Theorem [37]. Suppose we have given the m-th source data distribution Ps... a hypothesis set
H. and prior distribution over the hypothesis space θ. For any E (0, 1] and X >> 0, with probability at least T over the source samples Sm ~ for all posteriors we have:
Eр(H) [CF]] S Bx(R)) [S... (H)) X KI(plTT) + log 1 + VVs.
(4)
where V Smi. (2, n1) = log Bs, (
Lemma [38]. The PAC-Bayes bound, involving constants T and n, as introduced in Theorem 1, is minimized by the Bayesian posterior p(H), which represents the distribution O.
Proof. The
's change of measure states that for any measurable function - R,
we have:
(,, S KT(pp(=) - log (x (+))..
(5)
Thus, with 6(H) = A(L(H L(0,S,.) and Vp over hypothesis space θ we have:
(H)) (((T(H)) - (HH > (BAcH) (T(H) Ep(r)) (T.
(6)
< < + log (eC (L((H)) L(H.S.m))
For the non-negative random variable (Sm) PTCHHle L(H,Sm))) we apply Markov's inequality on it, and have:
IP p(<
1 -sm s.. (5m)) 2 1-TT
This impiles that with probability at least 1 the choice of Sm Ps.. have Vp over hypothesis space O:
P E [(H)) < E EpCH) L$... (70) +
2 21-7, (8)
X [KT((p[(T) + log ! -. V (A, (d,
where S' (A, m) log (M)) Es.. Pm
(2(000)-2(10) and we prove the statement of the
Theorem 1.
15

---

D Generalization Bound
[ Suppose we have given the probability measures U MT E E(() of the m-th source feature fs... and the blended-target domain feature fT. hypothesis space ④ and a subspace H. e. Let F denote a fixed representation space and cffsm: fT) denote the adaptation cost. For the ideal classifier h' E H and any classifier h E H with Js... ~ VS.. and fT ~ VT, we have:
Lemma 2
1 _dHAH(US... VT).
cssm. (h,, h() CT (h),
(9)
2
where es.. and ET denote the error the m-th source domain and the error on the blended-target domain respectively, and ET = E}= cT, duan denotes the Hisstannenc
Proof. By the definition of d.S..-disttnce, we have:
dHAH(VSm (TT== =
sup Przzwss.. [h(x) + h (x)) - Pro + N' (x))]] h
(10)
22 sup Ics,, (h, cT (h, h11 2 ( h.VEH
Theorem 2. Based on Lemma 2. with the error of the ideal joint hypothesis n/ = es.. (h') + ET (h') which is sufficiently small constant, for any (0,1), with probility at least eoervery every H. ey (f) is bounded by the following terms:
2d log + (
cT(h) S Esm (h) + 2 _dnnniis............ UT) + 4V
+ n
(11)
y
where of = e... (10) +T is the ideal error for the classifier, which is a sufficiently small constant. b' the size of unlabeled samples.
Proof. From Lemma 2, we can obtain the following terms:
(h(h)= < (hh))) + ET(h, <h'' + ES.. (h, 1²) + lET(H, - ES (h,
S <c(' csm (h, h²) + dHSH(US..
ser(h') ES (h) + Esm (h') + ddaneessmm VT)
(12)
csm (h) + duannlss, uT) tny
2d log (2b)) ) + log 17.
S Es.. (h) +
2 dnan((s. PT) + VV
D'
Finally, the expected error on the blended-tare ttagett domain can be bounded by utilizing the expected measures of NWD on the joint distribution of multiple source and blended-targe domains.
E
Additional Experiment Analysis
In this part, we present more visualization results with extra comparison methods including DANN [20] and AMDA [29]. All experiments are performed on task b+c-i+p of the ImageCLFF-DA [40].
Distribution Analysis. The t-SNE [54] feature visualization results of extra comparison methods are illustrated in Figure I. Note that different color dots denote different domains. Compared to ResNet-50, due to the domain adversarial learning, DANN can better align the source and target domains. Furthermore, AMDA achieves better performance through multi-source and multi-target domain features. Due to style adaptation and uncertainty estimation and elimination, SAUE achieves the best performance. The features from the same class generated by SAUE are better clustered while those belonging to different classes better separated.
Confusion Matrix. The comparison of confusion matrices with extra methods are illustrated in Figure 2. Although DANN and AMDA achieve significantly progress compared to ResNet-50,
16

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Seatti
(a) ResNer-50
(b) DANN
AMAA
(d) SAUE
Figure 1: Visualization analysis of SAUE in task b+p >c+.. (Zoom in for clear visualization)
Velort
Yeluraan
Dioe
23g
23g betle
bette
(0) ResNet-S0
(b) DANN
AM)A
(d) SAUF
Figure 2: Confusion matrices of SAUE and comparison methods in task b+p +c+. (Zoom in for clear visualization)
cless loss
d. c_soss
2500
7500
10000
12500
15000
Figure 3: Loss functions with the increasing of iterations.
they still misclassified some classes, e.g., class "bike" is misclassified into class "motobike". In contrast, benefiting from the style adaptation and uncertainty estimation, SAUE generates more correct predictions which located on the main diagonal elements of confusion matrix.
Convergence. We further analyze the evolution of different loss functions with increasing iterations. The results are shown Figure 3. The graphical representation illustrates that all the loss functions in our approach effectively converge the training iterations increased. This convergence showcases the adaptability and reliability our approach in MBDA tasks.
17

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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The main claims of this paper can found the abstract and introduction, which accurately reflect the paper's contributions and scope.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification: The limitations of the work are discussed the Appendix A.
3. Theory Assumptions and Proofs
Question: For each theoretical result, does the paper provide the full of assumptions and a complete (and correct) proof?
Answer: [Yes]
Justification: We provide the theoretical analysis of the proposed method in Section 3.5 and the related proofs on Appendices and C.
4. Experimental Result Reproducibility
Does the paper fully disclose all the information needed to reproduce the main ex- perimental results the paper to the extent that affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided not)?
Question:
Answer: [Yss]
Justification: The source code of SAUE is provide in the Supplementary Material.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, described in supplemental material?
Answer: [Yes]
Justification: The source code of SAUE is provide the Supplementary Material.
6. Experimental Setting/Details
Question: Does the paper specify all the training and test details (c.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
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7. Experiment Statistical Significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer:
[Yes]
Justification: The paper reports error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments, please refer Section 4.2.
8. Experiments Compute Resources
Question: For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time exccution) needed to reproduce the experiments?
Answer: [Yes]
18

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Justification: The paper provides sufficient information on the computer resources, which are included in the Section 4.1.
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19


Reviews:
[{"review_id": "UeVLVNAnyg", "reviewer": "Reviewer_LwRV", "summary": "The paper addresses the setting of Blended target domain adaptation. The paper proposes style adaptation and uncertainty estimation for multi-source blended target domain adaptation. They propose to utilize the extra knowledge acquired from the blended target domain. Where a similarity factor is utilized to obtain target style information to augment the source features. During training, they utilize the information from the target domain with a scaling factor and Wasserstein distance between the source and target features. Moreover, they do not utilize the domain labels without which they claim to align the multi-source and blended target domains. They utilize common domain adaptation datasets to demonstrate the effectiveness of their method.", "questions": "– Can the authors demonstrate with the default version of DomainNet instead of utilizing a subset of it? Moreover, the inclusion of the VisDA dataset would also be useful. \n\n– Could the authors also provide detailed numerical results of Figures C and D for better clarity? \n\n– The idea of using the target and source features during the training process with a scaling factor and Wasserstein distance doesn’t seem novel. Moreover the paper doesn't cite [1][2] which makes the related work quite limiting. \n\n– The idea of selecting style information is not new. Moreover, it is not clear which layer has been utilized for the style information and what the motivation is to utilize the style content at a particular layer. \n\n– The distribution analysis can be moved to the appendix, which doesn’t offer significant insights of the method. \n\n– Akin to MCDA [12], could the authors utilize ResNet 101 instead of ResNet50 and compare the results for the default version of the DomainNet dataset? With powerful backbones, it would be interesting how the style features are still preserved. \n\n– Extensive implementation details have not been provided; the batch size of the blended target domain, learning rates, and which layers from the ResNet model have been utilized for this if their code will be released. Does the method necessitate a high batch size for the target domain? In this case, it might be a limitation.\n\nReferences:\n[1] Shen, Jian, et al. \"Wasserstein distance guided representation learning for domain adaptation.\" Proceedings of the AAAI conference on artificial intelligence. Vol. 32. No. 1. 2018.\n[2] She, Qingshan, et al. \"Improved domain adaptation network based on Wasserstein distance for motor imagery EEG classification.\" IEEE Transactions on Neural Systems and Rehabilitation Engineering 31 (2023): 1137-1148.", "limitations": "Yes.", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "– The problem setting of addressing multi-target distribution shifts with a source trained model is useful \n\n– Performance improvement is significant compared to the baseline methods.", "weaknesses": "The paper lacks extensive validation, clarity, and novelty. Elaborated below.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate the comments and questions you post. Here are our point-to-point responses.\n\t\n>**Q1:** Experiments on the DomainNet and VisDA datasets. \n\n**A1:** Due to time limitations, we provide comparisons with important and recent multi-source blended-target domain adaptation (MBDA) methods MCDA and DGWA on the DomainNet and VisDA datasets. Initial experiments are shown in Tables R1 and R2 (see PDF), which demonstrate the superiority of our SAUE method on these challenging datasets. We will give more comparisons in the revised version.\n\n>**Q2:** Figs. 2C and 2D. \n\n**A2:** Detailed numerical results for Figures 2C and 2D are provided for clarity (see Table R7 in the PDF).\n\n>**Q3:** Idea about feature learning and related works. \n\n**A3:** Most of the existing methods use target and source features based on the single source and single target domain adaptation (SSDA) or single source and blended target domain adaptation (BTDA), which do not consider MBDA setting. In MBDA, challenges include: 1) various distributions from multiple source domains may introduce potential conflicts. 2) The target distributions are mixed. 3) Due to different discrepancies between different source and target domains, it is challenging to align multiple source and blended-target domains simultaneously. Our efficient and novel solution is to utilize the Wasserstein distance to explicitly measure the similarity of low-level features between the source and target domains to construct a weight factor. \n\nThe innovations of our proposed method are: 1) a novel style adaptation strategy that selects suitable blended-target style information for feature augmentation of multiple source domains. 2) An uncertainty estimation strategy that utilized Dirichlet-based evidential model to fuse multiple opinions from multiple source domains, which more effectively exploit domain-invariant knowledge from multiple source domains. 3) The proposed SAUE method provides a novel solution for the challenging MBDA setting.\n\nWe have cited and discussed these two good methods [1], [2]. The revised parts, which will be added to the final version, are as follows: WDGRL [1] employs a neural network to estimate the Wasserstein distance between the source and target domains, optimizing feature representations to minimize this distance. Shen et al. [2] introduced an improved domain adaptation network for motor imagery (MI) classification and utilized Wasserstein distance to construct a domain adversarial learning strategy to handle EEG-based MI detection tasks.\n\n>**Q4:** Style information selection and motivation. \n\n**A4:** While the technique of selecting style information has been explored in other contexts, our contribution lies in the unified adaptation framework designed for MBDA. Given the challenge that different discrepancies exist between different source and target domains, directly utilizing feature augmentation techniques like random augmentation may aggravate the domain shift problem. Therefore, we designed a strategy that selects style information to augment the source domains’ features. Our approach constructs tailored style information selection with a novel similarity-based weighted matrix strategy for MBDA, effectively selecting suitable blended-target domain style information for specific source domain, showing empirical gains and component synergy.\n\nThe style information is extracted from the second bottleneck layer of ResNet and utilized in the third bottleneck layer. The motivation for utilizing style content at a particular layer is to leverage the style information of the blended-target domain to augment the features of the source domains. Previous work [3] has demonstrated that low-level features (e.g., intermediate features from the second bottleneck of ResNet) of deep neural networks (DNNs) primarily represent style information. Building on this insight, we extract the style information of the blended-target domain from the low-level features of ResNet and use it to augment the features of the source domains.\n\n>**Q5:** Distribution analysis. \n\n**A5:** We will move the distribution analysis part to the appendix in the revised paper.\n\n>**Q6:** Evaluation of different backbones.\n\n**A6:** We have analyzed the performance of our method using the ResNet-50 and ResNet-101 backbones in the default version of the DomainNet dataset (please see Table R2 in the PDF file). The experimental results show that our method achieves significant performance gains when utilizing powerful backbones (ResNet-101). We compared the performance gains of the style adaptation module with standard backbone (ResNet-50) and powerful backbone (ResNet-101). The results indicate that the performance gains (+2.9% and +2.3%) of the style adaptation module when integrated into the ResNet-50 backbone surpass those (+2.3% and +1.3%) when integrated into the ResNet-101 backbone.\n\n>**Q7:** Implementation details and batch size.\n\n**A7:** For better understanding, we provide more implementation details as follows. The batch size for the blended-target domain is 32, same as the source domains. The learning rate starts at 1e-3, updated by the LambdaLR strategy. We use an ImageNet pre-trained ResNet, replacing the last FC layer with task-specific FC layers. Our method does not require a high target domain batch size, only equal to that of the source domains. Extra validation experiments (see Table R3(b) in the PDF) show different target domain batch sizes do not significantly impact performance. Thus, the batch size for blended-target domain is not a limitation.\n\nThanks again for your meaningful questions.\n\nRefs:\n\n[1] Wasserstein distance guided representation learning for domain adaptation. AAAI 2018. \n\n[2] Improved domain adaptation network based on Wasserstein distance for motor imagery EEG classification. IEEE TNSRE 2023.\n\n[3] Mixstyle neural networks for domain generalization and adaptation. IJCV 2024.", "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": "XH2mdhYwnU", "reviewer": "Reviewer_cWFB", "summary": "The paper proposes a \"Style Adaptation and Uncertainty Estimation (SAUE)\" method for Multi-source Blended-Target Domain Adaptation (MBDA). The core objective of the SAUE method is to enhance the source domain features by leveraging style information from a blended-target domain, which is a mixture of multiple sub-target domains. The method employs a similarity factor to select beneficial target style information and integrates an uncertainty estimation technique to fortify the model's robustness. The proposed SAUE is evaluated on several domain adaptation benchmarks, including ImageCLEF-DA, Office-Home, and DomainNet, demonstrating superior performance over existing state-of-the-art techniques.", "questions": "Detailed questions refer to the weaknesses.", "limitations": "The limitations of the work have been discussed.", "rating": 5, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "It is demonstrated an impressive results on several domain adaptation benchmarks with a significant improvement.\nThe paper is well-organized and easy to follow.", "weaknesses": "1. Although the blended-target domain adaptation has attracted much attention, it is similar to the open-compound domain adaptation that has no annotation of the target domain.  Authors should compare and discuss the difference between the open-compound domain adaptation works.\n\n2. The proposed style adaptation and uncertainty estimation are motivated and effective ways to handle domain shift problems. However, the idea has been well studied in conventional domain adaptation [a], domain generation [b], and open-compound domain adaptation fields. Therefore, the proposed method seems to lack innovation. \n\n    [a] Informative Data Mining for One-shot Cross-Domain Semantic Segmentation\n\n    [b] Uncertainty modeling for out-of-distribution generalization\n\n    [c] Open Compound Domain Adaptation with Object Style Compensation for Semantic Segmentation\n\n3. In my opinion, the proposed style adaptation technique is not tailored for multi-source BTDA as it also works for single-source BTDA. Therefore, the statement \"the first work for MBDA\" seems to be weak.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks a lot for your efforts in reviewing the paper. Below, we respond to your questions in details.\n\n>**W1:** Comparison to OCDA. \n\n**A1:** The key characteristic of blended-target domain adaptation (BTDA) is that the target domain is a mixture of multiple sub-domains sharing the same category space. Compared to BTDA, open-compound domain adaptation (OCDA) handles a target domain consisting of multiple sub-domains, some known (open-set) and some unknown (compound-set). OCDA's challenge is adapting the model to perform well on both known and unknown sub-domains without annotations. In BTDA, the model learns domain-invariant representations generalizable across the blended-target domain. In OCDA, the model identifies and handles known sub-domains effectively while being robust to unknown ones. We will add this comparison and discussion in Section 2 of the final version.\n\n>**W2:** The innovation of SAUE. \n\n**A2:** IDM [a] introduced a style transfer technique to alleviate the confusion between some high-entropy prediction images and target images, they also utilized mean entropy and cosine similarity to estimate the uncertainty. However, IDM only considered the single source and single target domain scenario for semantic segmentation. DSU [b] inserted the uncertainty estimation of the feature means and feature deviation into an AdaIN module. However, DSU do not consider the more realistic scenario where the target domains are multiple and blended. OSC [c] constructed style compensation (SC) strategy mainly to augment the object feature for a semantic segmentation image by constructing the weight factor and discrepancy features. The weight factor of SC is calculated by representative-key and object features. However, OSC do not take model uncertainty and multiple source setting into account. \n\nDifferent from the previous methods [a], [b], and [c], in multi-source blended-target domain adaptation (MBDA), the distributions of source domains are diverse, while the target distributions are blended. Directly using style transfer techniques in the current BTDA scenario fails to capture domain-invariant representations among different source domains in MBDA. Our efficient and novel solution utilizes the Wasserstein distance to explicitly measure the similarity of low-level features between the source and target domains, thereby constructing a weight factor. We then use the weight factor to select target domain features that are more suitable for the source domains and use them for feature augmentation. In this way, our method can mitigate the impact of the domain-specific attributes. In addition, multiple source domains may generate multiple opinions in evidential deep learning (EDL) [d]. Thus, our method constructs an uncertainty estimation strategy that introduced a Dirichlet-based evidential model to fuse multiple opinions by using the Dempster-Shafer Rule, which is more beneficial to exploit valuable knowledge from multiple source domains.\n\nAlthough some methods (including the aforementioned three methods) introduced such techniques into semantic segmentation, there is no effective strategy to address the knowledge transfer between multiple source domains and a blended-target domain in a more complex and realistic MBDA scenario. To fully consider the above-mentioned issues, we proposed our style adaptation and uncertainty estimation method. Thus, compared most of the existed methods, the main innovations of our method can be summarized as: 1) the novel style adaptation strategy is designed to select suitable blended-target style information for feature augmentation of multiple source domains. 2) The uncertainty estimation strategy that utilized Dirichlet-based evidential model to fuse multiple opinions from multiple source domains, which more effectively exploit domain-invariant knowledge from multiple source domains. 3) As far as we know, our propose SAUE method is the first work that proposed for providing a novel solution for the challenging MBDA setting, which is a real and complex scenario. Meanwhile, we have provided a theoretical analysis and sufficient experimental validation of the proposed method.\n\n[a] Informative Data Mining for One-shot Cross-Domain Semantic Segmentation\n\n[b] Uncertainty modeling for out-of-distribution generalization\n\n[c] Open Compound Domain Adaptation with Object Style Compensation for Semantic Segmentation\n\n[d] Murat Sensoy, Lance Kaplan, and Melih Kandemir. Evidential deep learning to quantify classification uncertainty.\n\n>**W3:**  In my opinion, the proposed style adaptation technique is not tailored for multi-source BTDA as it also works for single-source BTDA. Therefore, the statement \"the first work for MBDA\" seems to be weak. \n\n**A3:** Although style adaptation works for BTDA, its integration within MBDA is designed to handle complexities unique existed in multi-source scenarios. Directly using target style information to augment source features can aggravate domain shift due to conflicts from diverse source domain distributions. Unlike previous style transfer modules, our style adaptation module selects suitable target style information for different source domains using the Wasserstein distance to construct a weight factor, creating a cohesive representation space and improving the adaptation process in a multi-source setting\n\nMoreover, there maybe some misunderstandings regarding our claim for “the first work for MBDA”. In the first point of our contributions, we state: “An approach SAUE is proposed to explore information from multiple source domains for BTDA. As far as we known, SAUE is the first work that proposed for MBDA, which can utilize more feature information from extra source domains to learn domain-invariant representations.” Therefore, we would like to clarify that the SAUE method is the first work specifically addressing MBDA not merely the utilization of style adaptation technique. We still appreciate the reviewer's detailed insights.", "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": "vTSkU0O7aq", "reviewer": "Reviewer_NQ8Y", "summary": "This paper works on Multi-source Blended-target Domain Adaptation setting, which learns a model from multiple source domain and evaluates the model in a mixed multi-target domain without access to the domain labels of target data. This paper utilize the style information of the blended-target domain with weight factor to enhance source domain features for feature augmentation. To avoid the negative impact of domain-specific information in multi-source data, this paper also uses KL loss to reduce the impact of incorrectly classified source samples. This paper also proposes a domain adversarial loss with the help of nuclear-norm 1-Wasserstein discrepancy to reduce the domain gap between source domain and target domain by reusing the classifier of task network. Extensive experiments on various benchmark and comprehensive analysis prove the effectiveness of the proposed method.", "questions": "Please see weakness part.", "limitations": "No limitation is discussed in the paper.", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. This paper is well motivated.\n2. The proposed method is evaluated with various benchmark and analyzed extensively.", "weaknesses": "1. The style adaptation method is not compared with prior works proposed for the same purpose like MixStyle.\n2. Whether Domain Adversarial Alignment without Domain Labels outperform domain adversarial learning by treating multi-source data as one single source domain and multi-target data as one target domain is not discussed.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you very much for your feedback and questions. We provide our responses below\n\n>**W1:** The style adaptation method is not compared with prior works proposed for the same purpose like MixStyle.\n\n**A1:** We have compared our style adaptation (SA) with some prior works for the same purpose, such as random augmentation (RA), MixStyle [a], and pAdaIN [b]. The results are shown in Table R3 (a) on the PDF file. Compared to these prior works, our SA method effectively selects more suitable style information to augment the source features, achieving superior performance.\n\n>**W2:** Whether Domain Adversarial Alignment without Domain Labels outperform domain adversarial learning by treating multi-source data as one single source domain and multi-target data as one target domain is not discussed. \n\n**A2:** We have performed comparison experiments based on your advice (please refer to Table R4 in the PDF file). The experimental results show that the performance of domain adversarial alignment without domain labels outperforms the performance of domain adversarial learning by treating multi-source data as one single source domain and multi-target data as one target domain.\n\nRefs:\n\n[1] Domain generalization with mixstyle. ICLR 2021.\n\n[2] Permuted adain: Reducing the bias towards global statistics in image classification. CVPR 2021.", "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": "eQkkDKQv0A", "reviewer": "Reviewer_Sfz7", "summary": "This paper introduces a novel challenge in domain adaptation: the problem of Multi-Source Blended Target Domain Adaptation (MBDA). In MBDA, target domain distributions are blended, necessitating a model that can leverage features from multiple source domains to perform effectively on the blended-target domain. To address this problem, a Style Adaptation and Uncertainty Estimation (SAUE) approach is proposed. SAUE utilizes a similarity factor to select style information from the blended-target domain, creating a better representation space. Additionally, it enhances model robustness by employing a Dirichlet-based uncertainty estimation model to handle the diverse distributions introduced by multiple source domains. The effectiveness of the proposed method is validated through extensive image classification experiments. A theoretical analysis of SAUE underscores its potential in solving the MBDA problem.", "questions": "Please refer to the Weaknesses part.", "limitations": "The authors have adequately claimed their limitations in Appendix B.", "rating": 7, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The proposed MBDA problem is highly relevant for real-world applications. Unlike existing DA problems, MBDA incorporates confusing domain scenarios that mimic real-world situations where the source domain distributions are various, and the target domain distributions are blended. This assumption adds an additional challenge for DA, as the model needs to align domains without domain labels of the target domain.\n2. The proposed method SAUE is innovative. The inclusion of style adaptation to enhance the source domain features is intriguing, where the similarity-based weighted matrix is cleverly used to select more useful target style information for constructing a better representation space.\n3. The uncertainty estimation component effectively addresses the distribution discrepancy issue introduced by multi-source domains. In addition, the authors construct an adversarial learning strategy that aligns domains without the requirement of domain labels.\n4. The paper is well-written with the theoretical analysis clearly articulated and thoroughly explained.", "weaknesses": "1. The paper mentions various terms related to domain adaptation in Sections 1 and 2, such as UDA, SSDA, MSDA, MMDA, MTDA, and BTDA. What are the main differences and relationships among these terms? The authors are encouraged to provide a table summarizing the main characteristics to distinguish these terms.\n2. In the ablation study, the comparison primarily focuses on highlighting the improvements of each proposed module. It is suggested that the authors compare different feature augmentation methods to further emphasize the superiority of the proposed Style Adaptation module.\n3. A minor point of interest is how the proposed method performs on other backbones, such as the Vision Transformer.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your positive affirmation and constructive comments. Below are our responses to the weaknesses and questions.\n\n>**W1:** The paper mentions various terms related to domain adaptation in Sections 1 and 2, such as UDA, SSDA, MSDA, MMDA, MTDA, and BTDA. What are the main differences and relationships among these terms? The authors are encouraged to provide a table summarizing the main characteristics to distinguish these terms.\n\n**A1:** We have summarized the differences and relationship among the mentioned domain adaptation problems in the PDF file (Table R5).\n\n>**W2:** In the ablation study, the comparison primarily focuses on highlighting the improvements of each proposed module. It is suggested that the authors compare different feature augmentation methods to further emphasize the superiority of the proposed Style Adaptation module.\n\n**A2:** We have introduced random augmentation (RA), MixStyle [a], and pAdaIN [b] techniques to further emphasize the superiority of the proposed SA module, please refer to the PDF file (Table R3 (a)).\n\n>**W3:** A minor point of interest is how the proposed method performs on other backbones, such as the Vision Transformer.\n\n**A3:** We have added experiments on the Office-Home dataset and utilized a new backbone, ViT. The results are illustrated on Tab. R6, which demonstrate that our method also effective when using the ViT backbone. We will add this comparison to the final version to further show the quality of our paper.\n\nRefs:\n\n[a] Domain generalization with mixstyle. ICLR 2021.\n\n[b] Permuted adain: Reducing the bias towards global statistics in image classification. CVPR 2021.", "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 and Area Chair,

We sincerely thank all the reviewers for their positive comments and helpful feedback, which have significantly improved the quality of this paper. We have uploaded the responses to each reviewer along with the one-page PDF.

In response to the comments, we have carefully revised and enhanced the manuscript with the following additional discussions and experiments:

1.	Additional experiments on the default version of the DomainNet dataset and the VisDA-2017 dataset.
2.	Comparison of different backbones (ResNet-50, ResNet-101, and ViT-B/16).
3.	Detailed numerical results for Figures 2C and 2D.
4.	More implementation details about the proposed method.
5.	Discussion about the related work.
6.	Comparison of different domain adaptation settings.
7.	Comparison of different feature augmentation methods.

We hope our response sincerely addresses all the reviewers’ concerns.

Thank you very much for your time and consideration.

Best regards,

Submission3121 Authors

Meta-review: This paper presents a Style Adaptation and Uncertainty Estimation approach to tackle the Multi-Source Blended Target Domain Adaptation problem, where target domain distributions are blended. During the rebuttal phase, the authors provided additional experiments and discussions on challenging backbones, datasets, and difficult UDA settings. Following the response and multiple rounds of discussion, the paper received four positive scores, including one accept, one weak accept, and two borderline accept. Considering the reviewers'  positive comments and the paper's contributions, the meta-reviewer gives the final recommendation of acceptance.
Decision: Accept (poster)