Test-time Domain Generalization for Image Super-resolution
Authors: Zaizuo Tang
Yu-Bin Yang
Venue: ICLR 2026 Poster (2026)
Keywords: Test-time domain generalization
Primary area: transfer learning, meta learning, and lifelong learning
TL;DR: We propose a test-time domain generalization method for SR tasks, which employs a codebook strategy to achieve pixel-level transfer of target domain sample features.

Abstract: Test-time domain generalization (TTDG) methods enhance the performance of neural networks on target domains by transferring the feature distribution of target samples to approximate that of the source domain, while avoiding the computational cost associated with fine-tuning on the target domain. However, existing TTDG methods primarily rely on style transfer strategies operating at a coarse granularity, 
which prove ineffective for pixel-level prediction tasks such as image super-resolution (SR). To address this limitation, we propose a multi-codebook based test-time domain generalization framework (MC-TTDG). Our method leverages both domain-specific and domain-invariant codebooks to achieve fine-grained representation learning on source domains, and performs pixel-level nearest-neighbor feature matching and transfer to accurately adjust target domain features. Furthermore, we introduce a voting-based strategy for optimal domain-specific codebook selection, which improves the precision of feature transfer through multi-party consensus. Extensive experiments across diverse data distributions, and network architectures demonstrate that the proposed method effectively transfers feature distributions for SR networks. Our code is available at https://github.com/ZaizuoTang/MC-TTDG.

Published as a conference paper at ICLR 2026
TEST-TIME DOMAIN GENERALIZATION FOR IMAGE SUPER-RESOLUTION
Zai-Zuo Tang, Yu-Bin Yang
State Key Laboratory for Novel Software Technology
Nanjing University Nanjing, China
tangzzo sangzz0smail. juu, edu. en, yargyabineerj.edd..co
ABSTRACT
Test-time domain generalization (TTDG) methods enhance the performance neural networks target domains by transferring the feature distribution of target samples to approximate that of the source domain, while avoiding the computational cost associated with fine-tuning on the target domain. How- ever, existing TTDG methods primarily rely on style transfer strategies operat- ing at coarse granularity, which prove ineffective for pixel-level prediction tasks such as image super-resolution (SR). To address this limitation, we propose a multi-codebook based test-time domain generalization framework (MC-TTDG). Our method leverages both domain-specific and domain-invariant codebooks achieve fine-grained representation learning on source domains, and performs pixel-level nearest-neighbor feature matching and transfer to accurately adjust tar- get domain features. Furthermore, we introduce a voting-based strategy for opti- mal domain-specift codebook selection, which improves the precision of feature transfer through multi-party consensus. Extensive experiments across diverse data distributions, and network architectures demonstrate that the proposed method ef- fectively transfers feature distributions for SR networks. Our code is available at https://github.com// wow///iiiiTTTTTT//MCTTTDDD
INTRODUCTION
In real-world scenarios, due to variations in imaging conditions, the distribution the test sample set (target domain) often diverges from that of the training (source domain) used for neural networks (Guo et al. 2025 Deng et al. 2025). This discrepancy, commonly referred to as domain shift, can significantly degrade the predictive performance of neural networks trained on the source domain
when applied to the target domain (Wang et al. 2025 Choe et al. 2025).
As illustrated in Figure conventional domain generalization methods (Lee 2025 Rathore et al. 2025) utilize samples from multiple source domains with divergent distributions as input. By learning nomain--nvariia representations across these source domains, they enhance the model's robustness on target domains with differing distributions. However, these conventional methods neglect the distribution of the target domain during testing, resulting underutilization of target domain information. In contrast, test-time domain generalization methods (TTDG) (Ma al.) [024] Meng et al. 2025 Nam & Lee 2025) preserve the distributional centroids of the source domains during training. During testing. they perform style transfer target domain samples to align their distribution with these source domain centroids. By transferring the distribution of target domain samples in this manner, TTDG methods eliminate the need for computationally expensive fine- tuning on the target domain while simultaneously improving the model's performance on target domain samples.
Low-level vision tasks, such as image super-resolution (SR), are widely employed in remote sensing imaging, image transmission, and even serve preprocessing steps for downstream tasks. However, due to discrepancies in imaging devices, domain shift also exists in low-level vision tasks, which
"Corresooniigg author

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Published as a conference paper at ICLR 2026
Test-time Domain Generalization
Domain Generalization
MC-TTDG(Qurs)
4
4:456
4
T
2
6
T
3
(4:
5
6
Training stage
Test stage
Training stage
Test stage
Training stage
Test stage
Trainable
Frozen network
i-th domain
Source domain center
Style transfer
i-th codebook
network
Figure 1: Comparison domain generalization methods. The domains to 3 represent the source domains, while domains 4 to 6 represent the target domains. Conventional domain generalization methods optimize the source-domain network to enhance its robustness. In contrast, TTDG methods optimize the target-domain samples by aligning their distribution with the source-domain distribu- tion, thereby improving the performance of models trained on the source domain when applied to target data. Existing TTDG methods typically preserve the source distribution using single cen- troid and perform adjustment via style transfer (As shown in Equation D. The proposed method (MC-TTDG) improves upon this framework by introducing -peciff codebooks retain domain-specific features and enabling pixel-level highly fine-graing mee-grained feature transfer.
Souite Domain
Terget Demain
Coclbcokk Trancle
Style Trarsdrr
Figure 2: Comparison of feature transfer methods. We compare existing style transfer and the pro- posed codebook-based transfer in aligning the target domain distribution with the source domain distribution. Due to its inability to perform fine grained adjustments at the pixel level. style trans- fer proves highly ineffective for low-level vision tasks such as SR, resulting in most transformed samples overlapping significantly with the original target domain samples. In contrast, the proposed codebook transfer method effectively aligns the target domain distribution with the source domain. (t-SNE (Maaten & Hinton 2008) is used for feature dimensionality reduction.)
hinders the performance of deep neural networks in these applications (Tang & Yang 2024 Ai et al. 2024).
However, applying existing TTDG methods to SR tasks faces the following three challenges:
Low granularity feature transfer. Existing test-time domain generalization meth- ods utilize style transfer (Huang & Belongie, (017) (as shown in Equation T to modify the mean and variance of target domain features, thereby approximating the feature distribution of the source domain. Such global feature transfer is well-suited for high-level vision tasks like image classifica- entire images. However, as shown in Figure 2 for
Challengel:
tion, which rely on abstract representations pixel-level tasks such image SR, style transfer fails to perform fine-graing adjustments for each individual pixel. Consequently, it is difficult to accurately align the target domain feature distribution with the source domain feature distribution in SR tasks.
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(a) Single-codebook
(b) Multi-codebook
Figure 3: Distribution Comparison. We employ t-SNE Maaten Hinton 2008 visualize the codewords in the codebook. It can be observed that the single-codebook method (a) compresses features from multiple source domains, resulting in the loss of domain-specific information. In contrast, the multi-codebook representation learning method (b) allocates dedicated feature space for each source domain, mitigating the loss of domain-specific information caused by compression. (In the multi-codebbok visualization, red, green, and blue represent the codebook distributions for the P. IMG, and Canon data branches, respectively, while purple indicates the distribution of the codebook.)
p' p²= 4e )o²
+ Jr
where 4², ut', o" and represent the mean and variance of the source domain and target domain, respectively, while p and p' denote the target domain sample distributions before and after transfer.
Solution 1: We introduce a codebook into the representation learning of source domain features. During inference, pixel-level optimal codeword matching and replacement performed on the tar- get domain features, thereby achieving highly fine-grained feature transfer of target domain samples.
Challenge 2: Loss of domain-specific feature information
Existing domain generalization methods employ single backbone network or codebook to learn representations from multiple diverse source domain distributions. However, shown in Figure such methods compress these diverse source distributions into a compact feature space, leading to the loss of domain-specific features.
We extend the single codebook to a multi-codebook framework, comprising a shared codebook (referred to as the domain-invariin codebook) for learning domain-invariant features, and multiple domain-specific codebooks dedicated to capturing domain-specific features. This method only effectively preserves detailed information from each source domain, but also enhances the robustness of deep networks through a disentangled learning strategy that separates domain -specii and
Solution 2:
features. (Solution 2 extends Solution and is referred as Representation Learning Strategy based on Multiple Codebooks, RLMC.)
Challenge Optimal selection of domain-specific codebooks
The introduction of multiple domain-specinn codebooks necessitates the selection of the most appro- priate domain domain-specific codebook for the target domain features during testing. Existing methods in related scenarios, such as Mixture of Experts (MoE), typically employ gating mechanism -fteen implemented classification network. However, these methods inadequately account for domain shift. As result, the classification network trained source domain samples performs poorly when applied to target domain samples with different distributions (as shown in Table 33. leading to suboptimal codebook selection and misguided representation of domain-specific features in the target domain.
Solution 3: We propose voting- voting-based strategy for optimal domain- specific codebook selection, guided by multiple source coomain-speciff features. This method effectively reduces the inaccuracy of the classification network through multi-party voting.

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Our main contributions can be summarized as follows:
We introduce codebook perform pixel-level, highly fine-grained feature transfer on the target domain samples, significantly enhancing the efficiency of feature transfer for low- level vision tasks (SR tasks). To the best of our knowledge, the proposed method is the first test-time domain generalization method designed for low-level vision tasks, and also the first to incorporate codebook into test-time domain generalization.
We propose Representation Learning Strategy based on Multiple Codebooks (RLMC), which mitigates the loss of domain-specific information from the source domains. Addi- tionally, address the challenge of selecting the optimal domain-specific codebook during testing, we introduce a voting- based strategy for optimal domain-speciie codebook selec- tion.
Extensive experiments have been conducted to evaluate the proposed method across various network architectures and diverse sample distributions. The results demonstrate that our method significantly improves the efficiency of feature transfer in target domains for low- level vision tasks.
Refer to the Appendix A for related work on codebook and TTDG methods.
2 PROPOSED METHOD
2.1 PROBLEM SETTING
Given a source domain set D consisting of multiple source domains with diverse data distributions, denoted as D {Di, Dš..... ]. Each source domain comprises paired LR and HR images: DJ = (LRG, " Similarly, the target domain set DJT contains multiple target domains with varying distributions: D = [DT DJ ..... DT }. Unlike the source domains, each target domain contains only LR images: D. (LK) Aee and the source and target domain sets are disjoint: D D 0. n, and nr represent the number of source domains and target domains, respectively. Nas and Feee denote the number of samples in the source domain and target domain, respectively. A model M is trained on the source domain set D
Consistent with existing TTDG methods, our goal is to transfer the distribution of the target samples (LR")) to approximate that of the source domain samples (LR*), thereby enhancing the performance of the source domain-trained model M the target domain.
2.2 OVERVIEW
As shown in Figure the overall pipeline of the proposed method consists of two stages: During the training stage on the server side, pre-trained network weights (comprising Convl, Backbone, and Decoder, which together form a complete network architecture) are first loaded and frozen. The proposed Representation learning strategy based on multiple codebooks (RLMC) (Section 2.3) is then applied to implicitly learn both domain-invariant features and domain-specific features from the source domains. (It is worth noting that the primary optimization objective during the training stage is to endow RLMC with the ability reconstruct source domain features.) In the testing stage on the edge device side (Section 2.4), the learned comain-invarian codebook utilized to transfer the nomain-invariant features within the target domain sample distribution. Meanwhile, voting- based optimal domain-specific codebook selection strategy (Section (2.3) is employed to choose the most suitable domain-specific codebook for the target domain samples, followed by corresponding transfer of the domain-specific features in the target domain.
2.3
TRAINING STAGE (REPRESENTATION LEARNING STRATEGY BASED ON MULTIPLE CODEBOOKS, RLMC)
Existing methods typically employ single backbone network single codebook to represent features from multiple source domains with different distributions. This practice compresses the features of various domains into constrained space (as shown in Figure 3) resulting in the loss of
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(a) Training Stage
RLMC
Server side
Pre
Lass St
Loce
Rackbone
Decoder
Convi
Conv2
Loss Malaeaa
LR
Loog Loog
SR.
HR;
Loss
Codebook
Elament-en so
Geteed
Frezsn
L.R
Vote
Vote
apposa
Scation
counter
LR
(b) Testing Stage
Codeeooo Sav
Edge device side
Figure 4: Overall Framework. n, denotes the number of source domains. LR,, SR and HR denote the LR image, super-resolution image, and ground-truth HR image of the i-th source do- main, respectively. LR" and SR²" represent the LR image and super-resolution image of the target domain. Codebook and Codebook-Spe refer to the domain-invariant codebook and domain- specific codebook, respectively. Definitions of other variables can be found in Equations
domain-specific features. To mitigate this issue, introduce multiple domain- codebooks to represent and learn features from the diverse source domains, thereby avoiding feature loss caused by excessive compression of source domain features.
Additionally, to enhance the network's 's efficiency in learning both domain invariant and domain- specific features, introduce additional shared codebook, termed the domain-invariant invariant code- book. Using this domain-invariam codebook as a base representation and the Nomain-spectio code- books offsets, each source domain can be represented efficiently and discriminatively.
As shown Figure aa, the LR image subset the i-th source domain, denoted as LRS, is first fed into shallow feature extractor (Conv 1) obtain shallow features f.- These features are then decomposed via convolutional operations (Conv 2) into Baae and S,S The feature /Bae is trans- ferred using the shared codebook DDomain-invariin codebook), while f. y.Spe is transferred via the domain-specific codebook. This method implicitly guides the convolutional (Conv 2) decomposition such that f.aaee captures f Spe retains domain-specific features.
features-encodd encoding cross domain shared features and
fi Conv1(LR,) )
p Base Conw2(fi),
(3)
s =s - Si-JiBase
(4)
pBQ "ransfer[ff" Codebook
(5)
Ss = T Transser((fs Spe Codebook Spe),
(6)
where both Conel and Conv2 denote convolutional operations, and Transfer refers to the fea-
ture transfer operation. Further details of the transfer process elaborated in Equation [5]
Badebook-Bast denotes the domain-invariant codebook. while Codebook; Spe represents the domain- specific codebook corresponding to the i-th source domain. Both codebooks consist of multiple codewords, with Codebook = [Codewordy E b where N. is the number of codewords in Codebook Bose Bose and D denotes the dimensionality of each codeword, which matches the number of channels in the target domain sample features. The structure of Codebook to that of Codebook
Spe is defined similarly

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The transferred domain-invariam features f and domain- specific features fe are combined via element- wise summation and then passed into subsequent network layers to generate the correspond- ing SR image SR..
SR = Decoderd (BagdeerBBabboneeeeee
{(0)),
where Backbone and Decoder denote the backbone network and the decoder module, respectively.
Additionally, we introduce auxiliary classification branch that utilizes the input domain -specic features 1- identify the category of the current domain (as formulated in Equation (). It is worth noting that the predictions from this classification network are not utilized during the training stage.
Pre = CN (T
where Pre E BR,,, represents the category information predicted by the classification network CN, with and n, denoting the batch size and the number of source domains, respectively.
The overall training loss consists of four components: the image SR loss LossSR the quantization loss Loss"4 the commitment loss Losge and the classification loss Loss formulated as follows:
Comm Loss = Loss SR + >LLoss V3 + BLoss Comm LLLos Cls
(9)
Loss -SR - HR
(10)
Loss = ||sg(f, Basse) - f (.221 + || - 11: Loss Comm ||sBBasc sg(S; (Q) || + 115 sg([; Il
(11)
(12)
loss CrossSEnropp Pre, i),
(13)
where S SR. and HR denote the predicted SR image and the corresponding ground-truth HR image of the i-th source domain, respectively. The operator sg() represents the gradient stop operation. Loss" denotes the quantization loss, which is used to train the codewords in the codebook, while Losge constrains the network layers to align with the codewords. The terms 8, and ? are constant weighting coefficients.
2.4 TESTING STAGE
Existing test-time domain generalization methods typically rely on global style transfer to align the feature distribution of the target domain with that of the source domain. However, such global adjustment operates at a low granularity, making it difficult to perform effective feature transfer for pixel-level tasks like image SR. To address this limitation, we employ a codebook represent the source domain and propose a nearest-neighhor matching strategy to achieve pixel-level transfer of target domain samples.
As shown in Figure bb). the target domain image LR. is fed into shallow feature extractor (Convl) to generate shallow features. Convolutional operations (Conv2) then decompose these shallow features into domain-invariant features and domain- variant features. The domain-invariant codebook is utilized to perform pixel-level feature transfer on the domain-invariant features of the target domain samples,
SParget = TTransfer(fBass Base Codebook Basey.
(14)
where Base IR H.C.H.W with B, C, H, and W denoting the batch size, number of channels, height, and width of the feature, respectively.
The feature transfer operation computes the most similar (ncarest-neighhor) codeword in the code- book to each pixel feature and replaces the corresponding target domain feature at that pixel location using the retrieved codeword. Taking the transfer of the pixel feature fz,y position x, y as an ex- ample, where
21, ... H} and E {1,2..... WJ. the procedure is as follows:
Trans er (Sz.g Codebook) Ck where k: = argmini I| Calla, (15)
C.ECodebook
where Ck and C2 represent the k-th and i-th codewords in the codebook.

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For the domain -specfe features, we employ the proposed voting-based dlmain--seccffi codebook selection strategy (Section[2.5) to choose the optimal comain-specific codebook, which is then used to perform feature transfer on the domain-specifie features,
Jarget = V Vote( Vote([Targell
(16)
where Vote denotes the proposed voting- based Nomain-speciife codebook selection strategy.
Finally, the transferred Nomain--specifi features and domain invariant features are combined via element-wise summation and fed into subsequent network layers to generate the SR image SR for the target domain.
SR Deccodr((Backoone Target + &Targe)))
(17)
2.5 VOTING-BASHE
SPECIFIC CODEBOOK SELECTION STRATEGY
The presence of multiple domain-specific codebooks raises the challenge of selecting the optimal codebook for feature transfer of target Nomain--specife features. Existing methods in related scenar- ios, such Mixture of Experts (MoE) (Yang et al. 2225 Zhu et al. 2025 Zamfir al.) 2024). fail to account for domain shift. These methods typically employ a classification network to discriminate among experts and select the one with the highest confidence score as the optimal choice. How- ever, we observe that due to the distributional discrepancy between the target and source domains, the classification network trained on source domain samples tends yield significant prediction er- rors (Table 3 This leads to the selection of a suboptimal domain-specific codebook, consequently guiding the transfer of target domain domain-specific features an erroneous direction.
Therefore, we propose to leverage multiple Nomain--speciff codebooks to perform preliminary trans- fer on the target domain features, generating multiple sample features that approximate the distri- bution of the source domain features. Since the quality of these transferred features varies, we introduce a voting strategy to stabilize the predictions of the classification network.
As shown in Figure cc.. the target domain-specific features are transferred using multiple source domain-specific codebooks and then fed into the classification network. The highest domain cate- gory corresponding to each transferred feature is counted. The domain-specific codebook with the highest number of votes selected as the final choice. In the event of a tie (i.e., equal votes for multiple domain categories), the voting result of the original untransferred feature is adopted as the final prediction. The overall procedure is summarized in Appendix B
EXPERIMENTS
3.1 EXPERIMENT DETAILS
Datasets, Evaluation, and Network Architecture: We extensively validate the proposed method
on the DRealSR (Wei et al. 2020), Set5 (Bevilacqua et al. 2012). Set14 (Zeyde et al. 2010), B000
(Martin et al. 2001). Urban (Huang et al. 2015). Manga 109 Matsuu et al. 2017). and DIV2K Timofte et al. 2017) datasets. The SRealSR (Wei et al. 2020) dataset comprises multiple data branches- P. IMG, Canon, Pan, Sony, and DSC-eace captured by different cameras with distinct data distributions, making it widely adopted in domain generalization and domain adaptation re- search. For performance evaluation, we employ PSNR, SSIM, and LPIPS metrics. To demonstrate the architectural generalization capability of the proposed method, we further verify its effective- ness on the AdaCode (Liu et al. 2023), HAT (Chen et al. 2023), and MambalR (Guo et al. 2024a) network architectures.
Training Details: We train the network using the Adam optimizer with an input image size of 48 x 48 pixels. The batch size is set to 16. The training is conducted on 4 x V100 GPUs.
3.2
ABLATION EXPERIMENT
3.2.1
ABLATION EXPERIMENTS ON DIFFERENT CODEBOOK SETTINGS.
To validate the effectiveness of the proposed method, we conducted an ablation study on the code- book configuration. As shown in Table I used the performance of the MambalR (Guo al.
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Table 1: Ablation experiments on different codebook settings
Pan
Sony
DSC
Method
PSNRT
SSIMT
PSNRT
SSIMT
PSNRT
SSIM1
Baseline
31.0263
0.8580
30.7220
0.8645
30.9117
0.8816
One codehook
31.1111
0.8573
31.1411
0.8750
30,9640
0.8776
w/o Domain invariant codebook
30.9097
0,8552
31.0144
0.8749
30.9043
0.8782
Domain specific codebook
& Domain invariant codebook
31.1571
0.8594
31,2937
0.8791
31.2118
0.8836
Table 2: Ablation experiments on different feature transfer methods
Pan
Sony
DSC
Methods
PSNR
SSIM
PSNRT
SSIM
PSNR
SSIM
Baseline
31.0263
0.8580
30.7220
0.8645
30.9117
0.8816
One center style transfer
31.0262
0.8580
30.7171
0.8644
30.9131
0.8816
Multi center style transfer
31,0261
0.8580
30.7166
0.8644
30.9118
0.8816
Codebook- based transfer (Ours)
31.1571
0.8594
31.2937
0.8791
31.2118
0.8836
2024a)
network-tran trained on the source domain set (P, IMG, and Canon data branches)-on on the test domain as the baseline.
We first evaluated the method that employs only a single codebook to represent all source do- main features (One Codebook). ing method effectively improves the network's performance on the target domain. By performing pixel-level feature transfer on the target domain samples, the distribution of the target domain can be transferred with relative effectiveness. However, using single codebook to represent multiple source domains inevitably leads to the loss of Nomain-speciff information (as shown Figure 3).
be observed that the codebook-based representation learn-
while the performance metrics are also lower compared to the multi-codebook method.
To validate the effectiveness of the iamaiininvvarint codebook, we evaluated the performance of the SR network in the absence of this component (denoted as w/o Domain-Invariant Codebook), where only a domain-specifie codebook was used for each source domain. However, such meth- ods overlook the inherent correlations among source domains and weaken the network's ability to discriminate between domain-spechiee and Nomain-invarian features.
The proposed feature representation learning method, which jointly leverages Nomain-invariian and domain-specific codebooks, enables fine-grained feature representation for each source domain and avoids the loss of domain-spectie information associated with single-codebook designs. By disen- tangling the representations of domain- invariant and -specffc features, the method enhances the robustness of the domain-invariant codebook diverse input features, while the domain-specific codebook becomes more specialized in capturing domain-specific features, leading to improved ef- ficiency target domain feature transfer. The proposed method achieves notable performance gains across all three data branches, with PSNR improvements of 0.1308 dB, 0.5717 dB, and 0.3001 dB on the Pan, Sony, and DSC branches, respectively.
domain-invariant
3.3 ABLATION EXPERIMENTS ON DIFFERENT FEATURE TRANSFER METHODS
As shown in Table we evaluated the transfer efficiency of different feature transfer methods on the target domain features. First, we tested the single-center style transfer method, which employs unified style center across all source domains and adjusts the target domain distribution via mean and variance matching (as formulated in Equation T]. We also evaluated the multi-center style transfer method, where each source domain is represented by its domain-specifi mean and variance. During inference, the nearest source domain style is selected to transfer the target domain features. The results indicate that style transfer is highly inefficient for low-level vision tasks, with almost improvement performance metrics.
In level vision tasks, the features learned by the network represent abstract interpretations of the entire image, and predictions rely heavily on the global style of these features. Style transfer can thus effectively influence the model's predictions. However, low-level vision tasks require pixel-wise
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Table 3: Ablation experiments on different domain-specific codebook selection methods
Pan
Sony
DSC
Methods
Topl
Top21
Toplf
Top21
Topli
Top21
Maximum predicted score
0.2907
0.5673
0.0596
0.1391
0. 0.1660
0.3868
Voting selection (Ours)
0.3726
0.7838
0.3545
0.8757
0.4922
0.7800
Table 4: Performance comparison with other methods
Pun
Sony SSIM
DSC
Methods
PSNRT
SSIM
LPIPS
PSNR1
LPIPS
PSNRT
SSIM
LPIPS
TF-Cal (Zhan et al.
2022
28.85
0,7862
0.4318
27.92
0.7787
0.4387
29.51
0.8483
0.3690
TSB eran 2025
30.15
0.8315
0.4155
29.28
0.8044
0.4309
30.78
0.8739
0.3709
DG-PIC
ci al. 2024
29.71
0.8135
0.4130
30.06
0.8305
0,4455
29.88
0.8470
0,4463
TTDG Zhou 3m A144
29.71
0.8135
0.4463
30.06
0.8305
0.4455
29.88
0.8470
0.4130
TTMG Nam & Lec 2025
30.26
0.8411
0.4523
30.56
0.8541
0.4069
30.70
0.8776
0.4128
MC- MC-TTDG (Ouis)
31.15
0.8594
0.3593
31,29
0.8791
0.3157
31.21
0.8836
0.3583
predictions, depending fine-gratt fine-grained features of each individual pixel. Style transfer, operating at a coarse granularity, fails to transfer pixel-level features effectively. In contrast, the proposed codebook-based feature transfer strategy computes the nearest codeword in the codebook for each pixel feature and replaces the target domain feature at the corresponding pixel location using the retrieved codeword, thereby achieving extremely high granularity in feature transfer. As result, the codebook-basee method proves far more efficient for low-level vision tasks.
3.4 ABLATION EXPERIMENTS ON DIFFERENT DOMAIN-SPECIPE CODEBOOK SELECTION METHODS
As shown in Table we evaluated the accuracy of different strategies for selecting the optimal domain-specific codebook. We first tested the performance of each target domain image when using different domain-specific codebooks, then ranked the results. The domain-specific codebook that yielded the highest performance was defined as the optimal one.
Subsequently, evaluated the accuracy of directly using a classification network to select the op- timal domain-speciff codebook that is, choosing the codebook with the highest prediction score. However, due the distribution shift between the target and source domains, the classification net- work trained on the source domain exhibits low accuracy on the target distribution. This leads to misguided transfer of the target features and poor performance of the neural network the target domain (Line of Appendix T5)
The proposed voting-based strategy for optimal domain-specific codebook selection leverages mul- tiple domain-specific codebooks to perform preliminary transfer on the target domain features, thereby facilitating more reliable predictions by the classification network. By aggregating mul- tiple predictions through voting, this method enhances the accuracy of the selection process.
3.5
COMPARATIVE EXPERIMENT
As shown Table 4 we implemented existing test-time domain generalization methods for low- level vision tasks, including TF-Cal ZZhao et al. 2022). TSr al.) (023), DG-PIC(Jiang cct al.) 2024). TTDG et al.) 2024). and TTMG style transfer-an operation with coarse granularity for target domain feature distribution transfer. Consequently, they are unsuitable for low-level vision tasks requiring pixel-level predictions. In contrast, the proposed MC-TTDG method utilizes shared -invainnn codebook and domain- specific codebooks to achieve pixel-level feature representation and transfer, significantly enhancing the performance of SR networks the target domain.
Lee 2025). These methods employ
CONCLUSION
In this paper, propose test-time domain generalization method for SR tasks, which employs codebook strategy to achieve pixel-level transfer of target domain sample features. Our proposed representation learning strategy based multiple codebooks utilizes domain-invariant codebook

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and multiple domain-specifes codebooks enable fine- fineggrained representation of source domain samples. This method not only prevents the loss of domain-specifer features but also enhances the robustness of the domain-invariant codebook and improves the domain specificity of the domain- specific codebooks. Furthermore, the introduced voting-based strategy for optimal domain-specific codebook selection effectively mitigates the inaccuracy of the classification network caused by do- main shift through multi-party voting, thereby providing stable and appropriate transfer directions for target domain sample features.
ACKNOWLEDGMENTS
This work was supported by the Natural Science Foundation of China (Grant 62176119).
REFERENCES
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A RELATED WORK
A.1 CODEBOOK
Chen et al. (2022) first proposed a codebook-based SR network. During training, high-resolution (HR) image features were quantized and stored in the codebook. At inference, the network retrieved quantized HR features that closely matched the input low-resolution (LR) image features, signifi- cantly improving the SR performance of the SR network. Zhoou et al. 2022) proposed a codebook- based face reconstruction network that utilized a Transformer predict the optimal codebook entry for each pixel feature. They further introduced residual structure to incorporate LR image fea- tures into the decoding module, effectively preventing the loss of texture details. Liu al. (2023) argued that existing methods employed a single codebook for all scenarios, where one codebook needed to simultaneously encode diverse scenes (e.g., indoor, outdoor, and facial images), result- ing in feature confusion. To address this, they proposed a multi-codebook strategy that assigned dedicated codebook to each specific scene, significantly improving the feature encoding efficiency of the codebooks. Lu et al. (2024) addressed the image compression-decompressi proposing a HybridFlow dual-stream framework. This method combined a discrete stream based on high-quality codebook (ensuring perceptual quality) with ultra-low bitrate continuous feature stream, achieving fideliiiyiity image reconstruction at high compression ratios. Zhang et al. (2024) incorporated codebook into lasion-language models, where the codebook effectively modeled the
problem by
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correlation between image features and semantic features, thereby significantly enhancing the bustmess of VLM models. Li et al. 2024b) introduced codebook into the fake face detection task by comparing the discrepancies between codebooo-recconstuutte faces and authentic faces, which enabled effective identification of synthetic faces. [Wen et al. (2025) identified that existing meth- ods faced two key limitations: (1) nearest-neiighbor-baeee feature matching methods suffered from erroneous feature matching, and (2) multi-codebook methods tended to produce over-smoothed fea- tures. To address these issues, they first employed Top-K feature matching to retrieve the K most relevant codebooks, then performed feature fusion across these K codebooks before conducting fi- nal feature matching. Wu et al. (2025) introduced codebook into the low-light image enhancement task. Long et al. (2025) applied codebook to domain generalization tasks, where they theoretically demonstrated that discretized features from codebook exhibited superior robustness compared to continuous features.
Although a few multi-codebook methods (Liu et al., 2023 Wen et al. 2025) have been proposed, they learned representations sqquentially-trriine only one codebook a time for each source do- main. Furthermore, these methods did not explicitly incorporate the concepts of domain-speciffi and domain- invariant features. In contrast, the proposed method (MC-TTDG) performed representation learning simultaneously across multiple source domains (i.e., training all codebooks concurrently). It implicitly disentangled the features into domain-specift and domain-invariant representations, which not only enhanced the robustness of the network but also significantly reduces the training time required for the codebooks.
A.2 DOMAIN GENERALIZATION
Domain generalization methods enhanced model robustness by either explicitly or implicitly learn-
ing
and domain domain-specific features (Hu al. 2023; Guo et al. 2023 Vidit et al.
2023 Li et al., 2024a Cheng et al., 2024), or by constraining the consistency between network
outputs before and after augmentation Chattopadhyay
al. 2023 Wang et al. 2024 Long et al.)
2024 Guo et al. 2024b Ahn et al., 2024| Danish et al. 2024), therehy improving the network's
generalization capability against input degradations.
The Test-Time Domain Generalization (TTDG) method enhanced the performance of a source domain-trained network on target domains by aligning the target domain distribution with the source distribution during testing, while circumventing the substantial computational overhead associated with fine-tuning on target domain samples.
Zhao et al. (2022) postulated that amplitude features extracted through Fourier analysis represent domain-specific features. Consequently, they transferred the amplitude features of target domain samples toward the centroid of the source domain's 's amplitude distribution. Diang et al. (022)) em- ployed a meta-learning framework to partition the source domain into a meta- mnta--souree domain and a meta-target domain. Using individual meta-target domain samples along with the meta-source do- main, they predicted the overall distribution (mean and variance) of the meta-target domain. This method enabled the estimation of the real target domain's distribution, thereby facilitating feature normalization. Park et al. (003) addressed the issue of imbalanced sample sizes across source domains by first performing category- aware transfer between these domains. During testing. style transfer was applied exclusively to target domain samples that exhibited a large distributional dis- tance from the source domains, while those closer to the source domains were left unmodified. Diang et al. (2024) incorporated both global and local style transfer by applying global pooling to source domain point clouds to extract global features, while also dividing the point clouds into patches to capture local features. Through dual global and local adjustment of target domain point clouds, this approach effectively enhanced the efficiency of style transfer for target domain data. Yu & Hwang (2024) trained expert prompts using contextual image data and enhanced test samples by incorpo- rating these pre-trained prompts to guide the network toward accurate image classification. /Zhou et al. (2024) imposed constraints on the styles generated from the source domain to ensure orthogo- nality among them, thereby promoting diversity. Ma et al. (2024) reduced the distributional distance between the target and source domains by applying noisy masks to target domain samples. Meng et al. 2025) projected both source domain and target domain into a shared feature space, which enhanced the performance of deep neural networks on the target domain. Nam & Lee (2025) iden- tified domain- speccifc features through covariance analysis and adjusted the feature distribution of the target domain via style transfer.
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The aforementioned methods primarily targeted high-level vision tasks such image and point cloud classification, where features represent abstract interpretations of the entire image. By ap- plying style nranlfer-namlly, adjusting the mean and variance these methods successfully trans- ferred key predictive features in the target domain to align with the source domain distribution. How- ever, such global style transfer proved ineffective for low-level vision tasks like image SR, which require per-pixel prediction and are sensitive to fine-grained local features. To address this limita- tion, we introduced codebook-basee framework into test-time domain generalization. Specifically, we constructed a domain-spectie codebook to achieve high-fidelity representation of each source domain a fine-grained level. During testing, pixel-level feature transfer was applied to the target domain samples, which significantly improved both the efficiency of distribution transfer and the performance of deep networks on the target domain for low-level vision tasks.
B ALGORITHM
As shown Algorithm I we provide a detailed presentation of the voting- based domain-spectin codebook selection strategy using pseudocode.
Input: Classification network CN; domain-specific codebooks Codebook E {Codebook," 8p" th
domain domain-specific features farrees Vote counter Co E I2" where Co initialized as an all all-zero
vector;
Output: The transferred domain features /
for + to n. do
1,50 Trans fermsfer(r f Codebook,
Pre: CN().
Index; = Argmau (Prea((ree)) (Pres);
Co[[ndex.]] +=
end
A tie in votes then
Pre CN
Indeæ Argmaz(Pre)
10 end
11
else end
Inde: Argmar(Co)
SQ Target
Trans (J Sodebook Spe
Index
15
return STorget:
eQQ
Algorithm 1: Voting- based domain-specific codebook selection strategy
C ADDITIONAL ABLATION EXPERIMENTS
C.I ABLATION EXPERIMENTS ON DIFFERENT DOMAIN-SPECIRIC CODEBOOK SELECTION METHODS
Table 5: Ablation experiments on different domain sppciifc codebook selection methods
Pan
Sony
DSC
Methods
PSNRT
SSIM
PSNR
SSIMT
PSNRT
SSIM
Baseline
31.0263
0,8580
30.7220
0.8645
30.9117
0.8816
Maximum predicted score
31.0495
0.8584
30.6124
0.8667
30.9339
0.8799
Minimum distance
31.0203
0.8573
30.6992
0.8675
30.9578
0.8794
Voting selection (Ours)
31.1571
0.8594
31.2937
0.8791
31.2118
0.8836
evaluate the performance of the SR network under different domain- specific codebook selection strategies. First, we test the performance when using a classification network select the optimal domain-specific codebook (denoted as Maximum Predicted Score).
As shown in Table 5
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Table 6: Ablation experiments on domain-specific features
Pan
Sony
DSC
Methods
PSNRT
SSIM
PSNR1
SSIM
PSNRT
SSIM
Bascline
31,0263
0.8580
30.7220
0.8645
30.9117
0,8816
w/o domain-specifie feature
30,3096
0.8452
30.8673
0.8743
30.2964
0,8671
w/o domain-specifie feature transfer
30.9597
0.8586
30,9966
0.8726
31.1124
0.8831
Ours
31.1571
0.8594
31.2937
0.8791
31.2118
0.8836
Due to the significant domain shift between the target domain and source domain distributions, the classification network trained on the source domain exhibits low accuracy on target domain samples (Line of Table B}. This results in incorrect transfer of the target sample distribution, ultimately degrading the performance of the SR network on the target domain.
Additionally, we evaluated the method of directly computing the minimum-distance codeword across all codebooks for each target domain pixel feature and replacing the current pixel feature accordingly (Minimum distance). However, this method demonstrated poor performance, as the fusion of features from multiple domains disrupts the prediction behavior of the network.
Our proposed voting-based based Comain-speciffe codebook selection strategy first pre-transfers the target domain distribution to approximate that of the source domain. Through multi-party voting, it sig- nificantly enhances the accuracy of domain-specific codebook selection (Line of Table 33, thereby effectively improving the performance of the SR network on the target domain.
C.2 ABLATION EXPERIMENTS ON DOMAIN-SPECIHL FEATURES
As shown in Table 6 to validate the effectiveness of domain-specific features, we first evaluated the performance metrics of the network in the absence of these features (w/o domain-specifie fea- ture). As illustrated in the first row of Figure []] the SR image becomes blurred when domain- specific features removed, whereas incorporating them significantly enhances figh-freueeccy details. This indicates that, for image SR networks, high-freet figeffrry textual details constitute major part of domain-specific features. Introducing domain-specifie features effectively improves the net- work's capability to reconstruct fine textual details. Furthermore, we conducted an ablation study on whether feature transfer is necessary for domain-specifie features, specifically testing the per- formance when using untransferred domain-specific features directly (w/o domain-specific feature transfer). Since the target domain- -specifce features were not transferred to approximate the source domain distribution, the subsequent source-domain-traine network layers were incompatible with these features, leading to degraded performance.
C.3 ABLATION EXPERIMENTS ON DOMAIN-INVARIAN AND DOMAIN-SPECIFIC FEATURE SEPARATION STRATEGIES
As shown in Table 7 we evaluate the impact of different separation strategies for Comain-invariann and domain-specitf specific features on network performance. Existing domain generalization methods for SR typically define high-frequency information as domain-specific features and low-frequency information as conain-invariint features, employing explicit separation strategies such Fourier transforms, wavelet transforms, or Nownsampliggupssappl// pagiissaoii operations. However, due distribu- tion shifts between test and training samples, these pre-defined separation strategies often demon- strate poor adaptability to domain shift, frequently leading to incorrect feature partitioning. In contrast, our implicit feature separation strategy-implle immyymmenteeed through one-to-one coomain-specifii codebooks for each source domain alongside a shared nomain-invariant Codebook-enabee the net- work to adaptively separate domain-invarian and domain-specifie features. This approach demon- strates stronger adaptability to domain shift compared to explicit separation methods.
C.4 ABLATION EXPERIMENTS ON THE NUMBER OF DOMAIN-SPECIHIC CODEBOOKS
As shown in Table g evaluate the effect of varying the number of domain -seeaic codebooks on network performance. Since MC-TTDG requires a one-to-one correspondence between source domains and domain-specific codebooks for implicit separation of domain-specifie and domain-
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Table Ablation experiments on -onaain-invariant and domain-specifie feature separation strategies
Pan
Sony
DSC
Methods
PSNR
SSIM
PSNRT
SSIMT
PSNR1
SSIM
30.5356
0,8468
30.7316
0.8762
30.5960
0.8699
Wavelet transforms
30.6811
0.8479
31.0207
0.8746
30.8287
0.8737
Fourier transforms
31.1054
0.8548
30.9231
0.8767
30.1373
0.8792
Ours
31.1571
0.8594
31.2937
0,8791
31.2118
0.8836
Table Ablation experiments on the number of domain-specific codebooks
Pan
Sony
DSC
Number
PSNRT
SSIM
PSNRT
SSIM
PSNR
SSIM
Baseline
31,0263
0.8580
30.7220
0.8645
30.9117
0.8816
31.0815
0.8582
31.0125
0.8708
31.0912
0,8824
2
31.1012
0.8586
31.2891
0.8732
31.1593
0.8831
3
31.1571
0.8594
31.2937
0.8791
31,2118
0.8836
5
31.1783
0.8592
31.3129
0.8795
31,2482
0.8834
invariant features, and considering that the DRealSR dataset contains maximum of six camera branches, we configure up to five camera branches as source domains and one as the target domain. The MC-TTDG method exhibits strong robustness to the number of domain-spectis codebooks, consistently improving performance on the target domain across different codebook quantities.
C.5 ABLATIONN EXPERIMENTS THE NUMBER OF CODEWORDS
As shown in Table evaluate the impact of varying the number of codewords network perfor- mance. Reducing the number of codewords inevitably diminishes the representational capacity of the codebook, thereby lowering the transformation accuracy of target domain features. In contrast, increasing the number codewords enhances the diversity of preserved source domain features in the codebook, leading to improved network performance metrics.
Table 9: Ablation experiments on the number of codewords
Pan
Sony
DSC
Number
PSNR
SSIM
PSNR1
SSIM
PSNR
SSIM
128
31.1054
0.8589
31.1052
0.8710
31.1723
0.8821
256
31.1571
0.8594
31.2937
0.8791
31.2118
0.8836
512
31.1612
0.8599
31,3001
0.8788
31.2192
0.8841
C.6 ABLATION EXPERIMENTS ON FINE-TUNING EXTENT
As shown Table we compare the effects of fine-tuning entire network versus fine-tuning only the codebook on overall performance. Since the codebook is not initialized from pre-trained weights, full-network fine-tuning would allow the uninitialized parameters to disrupt the pre-trained backbone. In contrast, our MC-TTDG method keeps all other network layers frozen, which ef- fectively preserves the backbone's 's stability while improving the codebook's 's learning efficiency for source domain features.
C.7 COMPUTATIONAL EFFICIENCY COMPARISON WITH TEST-TIME DOMAIN ADAPTATION METHIODS
As shown in Table we compare MC-TTDG with methods that similarly leverage test samples to enhance performance such as test-time adaptation approaches (e.g., SRTTA, IODA, DASR) that fine-tune the network using test data. Our method demonstrates significantly lower compu- tational demands. These alternative approaches require substantial training time, making network fine-tuning particularly challenging for edge devices in practical applications.
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Table 10: Ablation experiments Fine-tuning Extent
Pan
Sony
DSC
tine-tuning Extent
PSNRI
SSIM
PSNR1
SSIM
PSNRT
SSIMI
Entire network
30.8873
0.8559
30.6771
0.8695
30.7417
0.8777
Codebook
31.1571
0.8594
31.2937
0.8791
31,2118
0.8836
VDDC
DSRKN
CKZOH
ONRKO
KEVDO
SLVDC
DSRKN
CKZOH
DNRKO
KEVDO
CORES
V
D DSR K N
C OKZO O H
DNAK ( RODD
Figure 5: Visual comparison. The first and second rows show the images generated without and with the transferred Nomain-speciffi features, respectively. The third row displays visual representation of the difference between the first and second rows.
In contrast, MC-TTDG operates by aligning the target domain distribution with the source domain distribution. It performs only inference target domain samples without network fine-tuning, substantially reducing computational overhead real-world scenarios. This design makes MC- TTDG particularly suitable for resource-constrainee environments compared to test-time domain adaptation methods.
Table 11: Computational Efficiency Comparison with Test-Time Domain Adaptation Methods. The computations were performed on a single V100 GPU with a patch size of 48x48. Abbreviations: (Fine-Tuning), TTDA (Test-Time Domain Adaptation). TTDG (Test- Time Domain Generalization).
Methods
Methods
Time (Min)
Need Training
FT
Fine- Tuning
1380
Yes
DASR (Wii al.
2021
19.33
Yes
TTDA
IODA (Tang & Yang
2024
9.01
Yes
SRTTA (Deng et al. MC-TTDG
2023
10.75
Yes
TTDG
0.00135
No
C.8 ABLATION EXPERIMENTS DIFFERENT SAMPLE DISTRIBUTIONS
we validated the effectiveness of the proposed method across diverse data distributions, including the Set5 (Bevilacqua et al. 2012), Set14 (Zeyde et al. 2010), B B100 (Martin et al. 2001). Urban (Huang al.) 2015). Manga (09 (Matsui et al., 2017). and DIV2K (Timofte jet al. 2017 datasets. MC-TTDG achieves consistent performance improvements across all data distributions.
As shown in Table 12
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Published as a conference paper at ICLR 2026
Table 12: Ablation experiments on different sample distributions
B100
Mamga109
Set14
Methods
PSNRT
SSIM
PSNR
SSIM
PSNR
SSIM
Baseline
23.5493
0.6216
22.1986
0.7440
23.3874
0.6426
MC-TTDG
23.8877
0.6260
22.6995
0.7482
23.7331
0.6507
DIV2K
Urban 100
Set5
Methods
PSNRT
SSIM
PSNRT
SSIM1
PSNRT
SSIM
Baseline
21.5490
0.5731
20.5615
0.5904
24.9639 25.2720
0.7586
MC-TTDG
22.2340
0.6341
21.0803
0.6056
0.7546
C.9 ABLATION EXPERIMENTS ON DIFFERENT NETWORK ARCHITECTURES
The SR network consists of two branches: prioritizing pixel fidelity and the other emphasizing visual perception. We conducted experimental analyses on both branches. As shown Tables T3 and 14 the proposed method effectively enhances the performance of the SR network the target domain.
Table 13: Ablation experiments on perception--oriented SR Networks
Pan
Sony
Methods
LPIPS
Dists
FID.
LPIPS.
Dists
FID
AdaCode (Liu et al.
2023
0.2688
0.1451
25.12
0.2755
0.1594
30.47
AdaCode + MC
0.2634
0.1438
23.75
0.2579
0.1450
31.39
DSC
Methods
LPIPS
Dists.
FID.
AdaCode (Liu et al.
2023
0.2480
0.1340
25.70
AdaCode MC
0.2417
0.1302
23.79
Table 14: Ablation experiments on fidelity- Seeiit--rrieneed SR Networks
Pan
Sony
DSC
Methods
PSNRT
SSIM
PSNRT
SSIMT
PSNRT
SSIMT
(hTe al. 2023 HAT + MC
31.0249
0.8607
31.0376
0.8763
30.9813
0.8837
31.0379
0.8593
31.1766
0.8779
31.1334
0.8826
MambalR (Guo et al. 2024a)
31.0263
0.8580
30.7220
0.8645
30.9117
0.8816
MambalR + MC
31.1571
0,8594
31.2937
0.8791
31.2118
0,8836
D VISUALIZATION RESULTS
As shown in Figure and Figure 7 we compared the proposed MC-TTDG with other test-time domain generalization methods. MC-TTDG demonstrated superior detail restoration and noise sup- pression.
E STATEMENT ON LLM USAGE
We used a Large Language Model (LLM), specifically ChatGPT, solely for language polishing and improving the readability the manuscript. The LLM was not used to generate ideas, conduct experiments, analyze results, contribute to the research methodology. All scientific content, in- cluding the conceptualization, design, implementation, and validation of the work, was entirely carried out by the authors.
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Published as a conference paper at ICLR 2026
TF-Cal: 29.71. TSB: 32.42,
DG-PIC: 30.87, TTDG: 33.00,
TTMG: 30.87, MC-TTDG: 33.72
EMAR
FL
EI カ
1.
TF-Cal: 27.62. TSB: 29.19,
DG-PIC: 28.72, TTDG: 30.45,
TTMG: 28.72, MC-TTDG: 32.51
-IF + +
TF-Cal: 28.47, TSB: 31.44,
DG-PIC: 33.66. TTDG: 32.84,
TTMG: 33.65, MC-TTDG: 34.99
Figure 6: Part of the visualization display diagram. The large image on the left is the LR image,
and the sub-images on the right are LR, TTF-Cal(Zhao et al. 2022), TSB(Park et al.) (023) and DG-
PIC (Jiang et al. 2024) row), GT, TTDG et al. 2024), and TTMG & Lee| (025) and MC-TTDC (Ours) (second row). The values following the name represent the PSNR metric of the current patch. Please zoom-in on screen. Please zoom-in on screen.
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Published as a conference paper at ICLR 2026
TF-Cal: 28.17. TSB: 30.59.
DG-PIC: 31.10, TTDG: 32.00,
TTMG: 31,10, MC-TTDG: 32.67
KOKK
3013
TF-Cal: 20.97, TSB: 22.20,
DG-PIC: 21.05, TTDG: 21.89,
TTMG: 21.05, MC-TTDG: 24.71
Figure 7: Part 2 of the visualization display diagram. The large image the left is the LR image,
and the sub-images on the right are LR, TF- Zhao et al. 2022). TSB Park et al. 2023) and DG-
PIC (Tiang al. 2024) row), GT, TTDG et al. 2024 and TTMG & Lee 2025) and MC-TTDG (Ours) (second row). The values following the name represent the PSNR metric of the current patch. Please zoom-in on screen, Please zoom-in on screen.
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Reviews:
[{"review_id": "a8QghtJDd9", "reviewer": "Reviewer_rKtr", "summary": "This paper introduces MC-TTDG, a framework for Test-Time Domain Generalization (TTDG) specifically designed for the task of image Super-Resolution (SR). They propose a novel approach that leverages a multi-codebook architecture to perform fine-grained, pixel-level feature transfer at test time. The core idea is to learn a shared, domain-invariant codebook and multiple domain-specific codebooks during training. At inference, features from a target domain image are adapted by replacing them with the nearest-neighbor codewords from the learned codebooks. A voting mechanism is introduced to select the most appropriate domain-specific codebook for an unseen target sample. The authors demonstrate through extensive experiments that their method significantly outperforms existing TTDG techniques when applied to SR across various datasets and network architectures.", "questions": "See Weakness", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- As far as I know, the paper is the first to formally address the problem of Test-Time Domain Generalization for image Super-Resolution. It astutely points out that domain shift in SR is a practical and significant challenge and that existing TTDG methods are fundamentally mismatched for such pixel-level tasks.\n- The proposed solution of using codebooks for pixel-level feature matching is an elegant and highly intuitive answer to the identified problem. The replacement of coarse style transfer with fine-grained codeword substitution is a logical and well-motivated design choice. The visual and quantitative results strongly suggest that this approach is far more effective than style-based methods for SR.\n- The authors have conducted a thorough set of experiments. They ablate every key component of their model, and demonstrate applicability across different SR architectures. This extensive validation bolsters the claim that the method is effective and generalizable.", "weaknesses": "- The central mechanism for separating domain-invariant and domain-specific features relies on a simple architectural split. While this is a functional design, it feels reminiscent of early, foundational ideas in domain generalization research. The field has since moved towards more sophisticated techniques. The paper does not engage with or advance this front; it instead applies a known, relatively simple technique to a new problem. While the application is novel, the core disentanglement method is not. \n- While the problem formulation is novel and the engineering is solid, the work feels more like a clever and effective application of existing ideas to a new domain. Although, I think integrating prior methods for other task is novel, there should at least be experiments showing how and why such prior method is the best while others do not fit. (e.g., why is feature separation by within the architecture is best for formulating the codebook rather than more sophistically separating the feature by training additional module?)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "jsqnuqeBVF", "reviewer": "Reviewer_xQKi", "summary": "This paper proposes a method based on multiple codebooks for test-time domain generalization specifically designed for the image super-resolution task. By leveraging domain-specific and domain-invariant codebooks to perform nearest neighbor feature matching and transfer at the pixel level, and by using a voting-based strategy to select the optimal domain-specific codebook, the effectiveness of the method was ultimately demonstrated on various SR test datasets.", "questions": "Please refer to the Weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.The writing is clear and easy to understand, and the methods are easy to implement.\n\n2.The generalization ability on various SR test datasets is impressive.", "weaknesses": "1. This paper does not report the additional parameter growth and training consumption required by the proposed method compared to the pre-trained model.\n\n\n2. This paper does not conduct a detailed comparison between this method of finetuning the pre-trained SR model and the effect of fine-tuning all the parameters of the model. Although this paper emphasizes that this is a method for domain generalization of super-resolution test datasets, adding additional training data and fine-tuning the full parameters of the original pre-trained model together will also bring certain benefits. Therefore, it would be best for this paper to demonstrate the effectiveness of the proposed finetune method in terms of efficiency and performance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "VDTNlxJlVp", "reviewer": "Reviewer_Kv5y", "summary": "This paper addresses the challenge of test-time domain generalization (TTDG) for low-level vision tasks, specifically image super-resolution (SR), where existing TTDG methods fail due to coarse-grained style transfer. The proposed framework, MC-TTDG, leverages multi-codebook representation learning and pixel-level feature matching to address three key limitations: low transfer granularity, loss of domain-specific features, and suboptimal domain-specific codebook selection. Key contributions include: (1) introducing a codebook-based pixel-level feature transfer strategy tailored for low-level vision tasks; (2) proposing a multi-codebook representation learning strategy (RLMC) that disentangles domain-invariant and domain-specific features to preserve source domain details; (3) designing a voting-based codebook selection strategy to mitigate domain shift-induced inaccuracies; and (4) being the first TTDG method explicitly designed for low-level vision tasks with codebook integration.", "questions": "1. The SOTA metric was not bolded in the article, suggesting an adjustment.\n2. How large are the codebooks (number of codewords, dimensionality)? Is there any tradeoff between codebook size and performance?\n3. How does the voting mechanism behave when there are many source domains (e.g., >10)? Is the method still stable under noisy or ambiguous domains?\n4. The paper states “Our code is available at ***” but no link is provided. For reproducibility, the exact URL should be included or committed to release upon publication.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. MC-TTDG is the first to adapt codebook-based representation learning to TTDG for low-level vision, addressing a critical limitation of style-based TTDG methods. The RLMC strategy’s disentanglement of domain-invariant and domain-specific features (via shared + domain-specific codebooks) is a creative combination of existing ideas, and the voting-based selection effectively mitigates domain shift in codebook choice—an unaddressed problem in prior multi-codebook work.\n2.  The experimental design is comprehensive: ablation experiments cover core components (codebook setup, transfer methods, selection strategies), baselines include state-of-the-art TTDG methods (e.g., TTMG, DG-PIC), and validation across diverse datasets/architectures demonstrates architectural generalization. Metrics (PSNR, SSIM, LPIPS) are standard for SR, ensuring result comparability.", "weaknesses": "1. The claim that RLMC disentangles domain-invariant and domain-specific features is not supported by direct evidence (e.g., feature clustering, ablation of domain-specific features’ impact on cross-domain generalization). Current evidence (Table 6) only shows domain-specific features improve SR quality, not that they are truly disentangled.\n2.Only one visual difference figure is presented (Figure 5/6), with little qualitative analysis.\n3. There may be shared structures or continuous variations (manifold-like variations) between domains, rather than discrete, mutually exclusive divisions; could the codebook partitioning method mislead the model to reinforce false domain separations?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "iKsBvGXBvw", "reviewer": "Reviewer_iQVi", "summary": "This paper proposes MC-TTDG, a multi-dictionary-based temporal domain generalization framework for testing image super-resolution (SR). Unlike conventional TTDG methods reliant on coarse-grained style transfer, this approach combines domain-specific and domain-invariant dictionaries to capture both global and subtle cross-domain variations. During the testing phase, pixel-level feature matching is achieved through a nearest neighbor codebook strategy. A voting-based mechanism selects the optimal domain-specific codebook, thereby enhancing robustness against distribution shifts.", "questions": "1. In multi-codebook settings, how might one diagnose and mitigate issues of codebook redundancy, collapse, or underutilisation? Are there scenarios—such as large-scale domain imbalance—where specific codewords fail to contribute effectively? How might this impact transfer performance and network robustness?\n2. Could quantitative results be demonstrated for domain transfer across datasets and in real-world scenarios (e.g., unseen camera types, fundamentally different image degradations), rather than being confined solely to the relatively controlled “branch” within DRealSR?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The proposed codebook-based feature transfer method achieves pixel-level alignment between target features and the source domain, thereby overcoming the limitations of previous style transfer approaches.\n2. By employing a multi-codebook architecture and introducing a voting-based strategy for selecting domain-specific codebooks, the reliability of model transfer can be enhanced, thereby circumventing the challenges encountered by expert selectors based on neural gating or classification.\n3. A first test-time domain generalization method designed for low-level vision tasks.", "weaknesses": "1. Can the computational overhead (execution time, FLOPs, and memory) of MC-TTDG relative to style transfer-based and other baseline TTDG methods be explicitly measured and reported during testing in resource-constrained (edge device) environments? Absent these details, claims about deployability are speculative.\n2. The article indicates that multiple codebooks are preferable to a single codebook.  How sensitive are the results to the number of source domains/codebooks? Have any failure cases been observed due to voting ties or misleading inputs? If so, what impact does the fallback mechanism have in such scenarios?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)