DUET: Dual-Perspective Pseudo Labeling and  Uncertainty-aware Exploration & Exploitation  Training for Source-Free Domain Adaptation
Authors: Jae Yun Lee
Jae Hyeon Park
Gyoomin Lee
Bogyeong Kim
Min Hee Cha
Hyeok Nam
Joo Hyeon Jeon
Hyunse Lee
Sung In Cho
Venue: NeurIPS 2025 poster ()
Keywords: Source-Free Domain Adaptation
Pseudo Labeling
Uncertainty-Aware Learning
CLIP
Primary area: general_machine_learning
TL;DR: We propose a source-free domain adaptation framework that refines pseudo labels using task-domain agreement and adapts CLIP with uncertainty-aware optimization based on proposed Tsallis mutual information.

Abstract: Source-free domain adaptation (SFDA) aims to adapt a pre-trained source model to an unlabeled target domain without requiring labeled source data. 
In a self supervised setting, relying on pseudo labels on target domain samples facilitates the domain adaptation performance providing strong supervision. 
However, a critical problem of this approach is the inherent instability of the pre-trained source model in the target domain, leading to unreliable pseudo labels for the target domain data. 
To tackle this, we propose a novel Dual-perspective pseudo labeling strategy that jointly leverages a task-specific perspective and a domain-invariant perspective, assigning pseudo labels only to target samples on which the target model’s predictions and CLIP’s predictions agree. 
To further enhance representation learning without introducing noisy supervision, we apply consistency training to uncertain samples.
Additionally, we introduce a Tsallis mutual information(TMI)-based vision optimization strategy guided by an Uncertainty-based adaptation index (UAI), which dynamically modulates entropy sensitivity based on the model’s adaptation uncertainty. 
The UAI-based training paradigm enables stable and adaptive domain alignment by effectively balancing exploration and exploitation processes during the optimization process. Our proposed method achieves state-of-the-art performance on domain adaptation benchmark datasets, improving adaptation accuracy by 1.6% on Office-Home, 1.4% on VisDA-C, and 2.9% on DomainNet-126, demonstrating its effectiveness in SFDA.
The code is publicly available at https://github.com/l3umblee/duet-sfda.

DUET: Dual-Perspective Pseudo Labeling and Uncertainty-aware Exploration & Exploitation Training for Source-Free Domain Adaptation
Jae Yun Lee" Min Hee Cha",
JacHyeon Park?,
Gyoomin Lee", Joo Hyeon Jeon'. Sung In Cho 1-
Bogyeong Kim Hyunse Lee".
Hyeok Nam
Department of Artificial Intelligence at Sogang University 35 Baekbeom-ro, Mapo-gu, Scoul 04107, Republic Korea {stcaft, skagur10, jhbox7, csi2267) /oogang........o ac. .r
2.3 Department of Computer Science and Artificial Intelligence at Dongguk University 30 Pildong-ro 1-gil, Jung-gu, Scoul 04620. Republic of Korea [pjhh011, exceleaf, sae4394) edu [qhrudd151, rninicha] ac kr
Abstract
Source-free domain adaptation (SFDA) aims to adapt pre-trained source model to an unlabeled target domain without requiring labeled source data. self- supervised setting, relying pseudo labels target domain samples facilitates the domain adaptation performance providing strong supervision. However, crit- ical problem of this approach is the inherent instability of the pre-trained source model in the target domain, leading to unreliable pseudo labels for the target do- main data. To tackle this, propose novel Dual-perspective pseudo labeling strategy that jointly leverages a task-specific perspective and a perspective, assigning pseudo labels only to target samples which the target model's predictions and CLIP's predictions agree. To further enhance representa- tion learning without introducing noisy supervision, we apply consistency training to uncertain samples. Additionally, we introduce a Tsallis mutual information adapta-
(TMe)-based vision optimization strategy guided by an Uncertainty-bac tion index (UAI), which dynamically modulates entropy sensitivity based on the model's adaptation uncertainty. The UAl-based training paradigm enables stable and adaptive domain alignment by effectively balancing exploration and exploita- tion processes during the optimization process. Our proposed method achieves state-of-the performance on domain adaptation benchmark datasets, improving adaptation accuracy by 1.6% on Office-Home, 1.4% on VisDA-C. C, and 2.9% on DomainNet-1226 demonstrating its effectiveness in SFDA. The code is publicly available http://ggthbbbcoom/mmmmleeeeeete
Introduction
explores training strategies or model architec- tures that enable model trained labeled source domain data achieve high task accuracy the unlabeled target domain data. Traditionally, UDA assumes access to both labeled source domain data
Unsupervised domain adaptation (UDA) 26, 3 6
"Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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and unlabeled target domain data. However, in practical scenarios, the application of UDA can be limited due to privacy concerns, legal regulations, and data security issues associated with labeled source domain data.
To address these limitations, source-free domain
1.0
Pscusoo Label Ratio
adaptation (SFDA) [12 I 28
29
30
38
II
0.9
Ours
1.0
Ouss
13 27
has been proposed, allowing domain adaptation without the use of labeled source do- main data. SFDA relies solely on a pre-trained source model and unlabeled target domain data achieve adaptation. SFDA can be broadly cat- egorized into two main approaches. The data-
Accurney
DIFO
0.8-
DIFO
0.8
0.6
0.7
04
0.6
3 (a) Pscudo label accuracy (b) Pscudo labeled sample ratio Figure I: Limitations of the existing state-of ofii--of-hee- the- art method [29] and comparison with the proposed approach on Clipart (target domain) of the Office- Home dataset. Comparison of (a) pseudo label ac- curacy and (b) pseudo label assignment ratio. The x-axis represents the training iterations.
0.2
focuses on con- structing a virtual source domain using adversar- ial learning techniques such as GANs 9 14
based approach 32
10.
39
g
or on extracting intrinsic feature information from existing data to facilitate adaptation to the target domain. In contrast, the model-driven approach [22] I [29] operates in a self-superrvieed learning paradigm, where supervision is derived from the target domain data based on the predictions (pseudo labels) generated by the pre- pre-trained source model. It generally outperforms data-based approaches and has become the most widely adopted paradigm in SFDA. A representative method of the model-driven approach is SHOT which generates pseudo labels for target domain samples based on feature space analysis. These pseudo labels are subsequently utilized for entropy minimization, enhancing the pre-trained source model's s performance the target domain. AdaContrast 1 performs batch-wise pseudo label genera- tion and refinement for test-time adaptation. It identifies the nearest neighbors of each target sample in the feature space based on cosine similarity and assigns pseudo labels by averaging the target model's s predicted probabilities. The pre-trained source model is then trained using a combination of supervised loss calculated by using pseudo labels and contrastive learning. These approaches provide strong supervision in unlabeled settings, facilitating domain adaptation. However, the pre-trained source model (i.c., target model) exhibits instability in the target domain, which Ieads to unreliable pseudo labels in the early training stages and makes it difficult for the target model establish a clear learning direction.
To mitigate this issue, 29) employs CLIP a lasion-language (ViL) model with strong zero-shot capabilities, to enhance the quality of pseudo labels. Additionally, it introduces a mutual information (MI)-based loss function that enables prompt learning CLIP and knowledge distillation between CLIP and the target model. Despite achieving state-of-the-ar performance on multiple benchmark datasets, DIFO still faces challenges in assigning accurate pseudo labels to target domain samples, particularly in the initial training stages. Moreover, while MI-based prompt learning facili- tates CLIP's adaptation to the target domain, its effectiveness is inherently limited by the fixed vision encoder extracting low-quality image features that lack clear inter-class separability.
Fig. [ compares the proposed method with DIFO in terms of pseudo label accuracy and the pseudo- labeled sample assignment ratio on the Office-Home 34] dataset. As shown in Fig. T DIFO suffers
The t-SNE visualization of image feature extracted from CLIP
Toy example
TMI
0.10
MI
0.05
0.00
1.00
0.75
0.50
0.25
0.00
(b)
(c)
Figure 2: Limitations of CLIP utilized in the existing method DIFO (a) and (b) show the t-SNE visualization of image embeddings extracted by CLIP's vision encoder before and after the proposed vision encoder optimization, respectively. (c) represents comparison of Mutual information (Mf) and Tsallis mutual information (TMJ) under varying noise levels (ar). The a-axis represents the decreasing noise levels, while the y-axis denotes the computed loss.

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from low pseudo label accuracy across the entire set of training samples. This issue hinders model convergence and introduces instability into the training process, Moreover, since CLIP not pre- trained for specific tasks but rather exhibits task-agnostic properties, its predictions tend to reflect general and ambiguous patterns rather than task- task-speeific distinctions. Therefore, a refinement process that accounts for this limitation is necessary. To this end, we propose two key strategies: (1) A learning framework that assigns pseudo labels only to samples whose predictions from the task-specific target adaptation model and the domain-invarian CLIP model agree. The remaining samples, which lack such agreement, are optimized using consistency-driver representation enhancement until sufficient feature quality is achieved. (2) A Tsallis mutual information (IMI)-based adaptation uncertainty- aware vision optimization strategy that dynamically adjusts training intensity based on the model's current adaptation uncertainty level, enabling robust adaptation even under large domain gaps.
In the first strategy, we refine the pseudo label generation process by assigning hard pseudo labels only to samples where the task-specific target adaptation model and domain-invarinn CLIP agree in their predictions. This selective assignment allows us to extract highly reliable pseudo labels, combining task relevance and domain generalizability. Meanwhile, instead of assigning noisy labels to the remaining samples, we apply consistency training to boost the feature representation ability of the target model. This approach enables effective use of unlabeled data while minimizing label noise, thus improving overall adaptation performance.
In the second strategy, unlike DIFO-which adapts CLIP to downstream tasks via text prompt tuning-we we directly fine-tune the CLIP vision encoder. Existing prompt tuning 24 35 can be limited when the image features extracted by the frozen vision encoder exhibit low clustering quality due to a large domain gap, as illustrated in Fig. 2[a). In this case, the text embeddings are forced to adapt to low-quality image representations, limiting performance. This problem becomes even more severe in SFDA, where no source data is available and the domain gap is large. To overcome this issue, propose alternative strategy: a TMI-based adaptation uncertainty-aware optimization method that enables effective feature alignment between the pre-trained CLIP knowledge and the target domain, thereby mitigating the limitations of prompt tuning. Specifically, existing prompt tuning is guided by MI-based loss function between the target model and CLIP predictions. While this encourages aligned predictions between the two models, the standard MI loss does not sufficiently reflect the model's 'ss current training state. We demonstrate this limitation with a simple toy example in Fig. cc. This toy example simulates the training process of model optimized using MI loss. In particular, we measure the MI between two probability vectors: one is a randomly generated probability vector (considered as ground truth, p), and the other is noisy version of this probability vector (considered as the model prediction, p). The detailed settings are provided in A As the noise is gradually reduced, we observe how MI changes between these two distributions. As observed in Fig. ci). MI struggles to capture the overall changes in the probability distribution as the noise level decreases, making flexible loss measurement challenging (i.e., MI shows little meaningful variation despite gradual noise reduction). To address this limitation, we propose an approach that adaptively considers the model's adaptation uncertainty during CLIP's vision optimization. Specifically, we introduce a TMI loss function based on Tsallis entropy [33] which leverages our uncertainty-base based adaptation index (UAI) to dynamically control the entropy curvature depending on the model's 's adaptation uncertainty. This UAI is used to compute TMI, allowing the model adjust the strength of training based on its current adaptation uncertainty level and guiding the optimization process in a stage-aware manner. As shown in Fig. [c). the UAI enables TMI to encourage the utilization of diverse samples during the early unstable training phase (exploration), and to shift toward a stable optimization strategy (exploitation) for SFDA scenarios with large domain gaps. Consequently, TMI provides an effective optimization strategy in SFDA scenarios where a significant domain gap exists. balancing exploration and exploitation to achieve robust domain adaptation. Our contributions are summarized as follows:
Appendix
We propose a Dual-perspective pseudo labeling strategy that integrates a task-specific perspective, which emphasizes cins-diicriminative semantics, and domain-invariant per- spective, which focuses on robust generalization, thereby enabling more reliable supervision and structurally enriched representation learning.
We introduce a Tsallis mutual information- vision optimization strategy that leverages an sicertainty-based adaptation index to dynamically regulate the optimization process by separating into exploration and exploitation stages depending on the model's s adaptation uncertainty level.
3

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Our method achieves performance improvements over state- state-of-the-ans art methods on Office- Home (+1.6%), VisDA-C (+1.4%), and DomainNet (+2.9%) datasets.
2 Preliminary
Consistency training Consistency training 25 36 20 is widely used technique in semi- supervised learning that ensures the model produces consistent predictions when different augmen- tations are applied to the same input sample. This technique is particularly effective in scenarios with limited labeled data, as it provides strong regularization effect, thereby improving model performance. In source-free free domain adaptation (SFDA), where adaptation must be performed on an unlabeled target domain without access to labeled source domain data, the reliance pseudo labels can introduce instability during training. By progressively aligning the model's s predictions on augmented target samples, this approach mitigates the impact of pseudo label noise and enhances the model's s ability to generalize to the target distribution.
Tsallis entropy The conventional Shannon entropy- based model training process can lead to over- confidence in the presence of label noise/4]]] [7]. Tsallis entropy [3] offers broader representational capacity in entropy computation compared to Shannon entropy, providing a potential solution to this issue. Tsallis entropy is defined as follows:
x'-1
(2) = 1 1-
Sp(p,) =
-- Epdnn(pi)
(I)
Here, (-) and S.)+) denote Tsallis logarithm and Tsallis entropy, respectively. pt represents the probability of event i in given discrete probability distribution. is known as the entropy index, controlling the degree of non-extensivity in the entropy formulation. As the entropy index approaches one, Tsallis entropy asymptotically converges to Shannon entropy. If is greater than one, the variation in entropy values with respect to probability changes smaller than in Shannon entropy. For intuitive understanding of how different values of entropy index q affect the shape of the entropy function, please refer to the visualization in Appendix B From a model training perspective, Tsallis entropy-based loss function with q larger than one reduces the model's ' sensitivity to high- confidence predictions and promotes exploration of uncertain samples. Conversely, when g is less than one, the entropy values change more sharply with the probability variations, leading to increase sensitivity to confident predictions, encouraging exploitation. This characteristic highlights that a loss function utilizing Tsallis entropy can effectively regulate the model's s learning pace. By adjusting the entropy index q, the model can emphasize exploratory learning under high uncertainty and shift toward stable optimization (exploitation) as confidence increases.
3 Methodology
Overview As shown in Fig. the proposed method is built upon three core components: 1) Calibrated pseudo label generation (CPG), which generates two types of pseudo labels (soft and hard) for target domain samples whose predictions from the target model and CLIP agree. 2) Dynamic entropy-guided vision optimization (DVO), in which the soft pseudo labels produced by CPG are used within the proposed Tsallis mutual information (TMJ) to optimize CLIP's vision encoder for the target domain. 3) Pseudo label matching (PLMatch) framework, which trains the target model by incorporating hard pscudo labels.
These core components are then devided into two hierarchical processes, a cycle-level process and an iteration-level process in consideration of computational cost and training stability. In the cycle-level process, components with high computational cost, such as CPG and DVO are executed, enabling the generation of reliable pseudo labels and the adaptation of CLIP's vision encoder. contrast, the iteration-level process consists of PLMatch framework, which repeatedly trains the target model with fixed hard pseudo labels from the current cycle. Specifically, our training framework organized into multiple cycles, each consisting of several iteration-level processes. Fach cycle first generates pseudo labels and optimizes the CLIP vision encoder, followed by several iteration-leva processes that train the target model with the fixed hard pscudo labels.

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Cycle-level process
Iteration-level process
CPG
DVO
PLMaleh
Prodiction Misture
Drukpeespecibe PL
Dynsmic unteupp-gukdd video optimlention
Pseudo Labal Matching frazowook
c((..)
Uniabeled data
de puth A+x,''sss pash
d'a) pullt
Classifier g
Target. f'.g
a(za)
A
y
a(zu)
Lrom
Text prompt
T
LTMI
A(ra)
Task specific Pred. 1
Vil.
Target,
Mivel prediction.
CPG Mixed prediction
Adaptation Index
A
Exploration
Exploitation
polly
Goneralinod y
Maintoln Moidl's lerring apaaiiity
a(z))
Encoerary higl vetingg
ViL
Preuddo labeled dasa (ry)
CPC
Fraining Fraeeiing
c(T.)))
Treining cycle
a(xy) A(zu): Weak and strong angmented image, respectively
Figure 3: Overall structure of the proposed method.
3.1 Cycle-level process
3.1.1 Calibrated pseudo label generation (CPG)
In the cycle-level process, CPG generates pseudo labels for training both the CLIP vision encoder and the target model (pre-trained source model) by combining their prediction probabilities. Note that we obtain two types of pseudo label (ø. 9) in CPG, performing prediction mixture (soft label, p) and perspectree pseudo labeling (hard label, g). Soft pseudo label obtained from prediction mixture will be used in DVO for performing vision optimization of CLIP. Hard pseudo label acquired from persepeeree pseudo labeling will be employed in PI.Match which is represented in Fig. for training the target model.
Prediction mixture To obtain the prediction mixture (p) which will be utilized in DVO as target label, the prediction probabilities for a target domain sample from CLIP (polip) and the target model (p") can be obtained as follows:
o sim(fy (a(x:)), 0²)
clip Pi
~l'' ol
(2)
T
where f (a(a;)) and 0r (E (K(x)))) represent the weak augmented image feature which is the output of CLIP vision encoder fv (.) and the text feature which is the output of CLIP's text encoder with sentence in the format of "a photo of [class] (33, respectively. f((.) and g'(:) are the feature encoder and classifier of the target model, respectively. <(.) and sim((.-) denote the softmax function clip and
and the cosine similarity function, respectively. r is a pre-trained temperature parameter (23 P: of denote the prediction vector (E IR for the i-th weakly augmented target sample a(x;) from CLIP and target model, respectively. is the number of classes and D the latent dimension. Subsequently, the mixed prediction for a target domain sample is obtained by averaging the predictions as follows:
p.
(p. clip + p
(3)
where 9i E R represents a soft pseudo label. Note that the soft pseudo label úE is utilized in DVO. which is the cycle-level process.
Dual-perspective pseudo labeling To ensure high-confidence pseudo labels and mitigate the risk of noisy supervision, we employ dual-peessppp persppeesee pseudo labeling strategy that selectively assigns pseudo labels only to samples where the predictions CLIP (f and the target model (p') are aligned. Formally, implement this selection mechanism using binary mask m,, where samples meeting the alignment criteria are assigned to pseudo labels, while others remain unlabeled:
f = 5(p:), m, = 1 [argmax (p: argmax (p:)
(4)
5

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Here, 8(-) and 1[-] are a one-ho hot encoding function and indicator function, respectively. m: = 1 if the predictions of both models are identical, and m, 0 otherwise. Note that the hard pseudo label Øi utilized in PLMatch which is the iteration-level process.
3.1.2 Dynamic entropy-guided vision optimization (DVO)
Uncertainty-basel adaptation index We define the uncertainty-baset adaptation index vi to dynamically control the entropy behavior of TMI in response the model's adaptation uncertainty. The & allows balancing flexibility (th > 1) and convergence (vh < 1) throughout training, and is directly incorporated into the computation of Tsallis mutual information. Specifically, likely with the entropy index in Eq. [ when the model exhibits high adaptation uncertainty, the adaptation index ei adjusted above one, resulting in flatter curvature of the entropy function and encouraging learning from low-confidence samples (exploration process). Conversely, as the model's 's uncertainty decreases, the adaptation index & reduced below one, which sharpens the curvature of the entropy function and promotes stable learning centered on sigh-confidence samples (exploitation process). For this, to comprchensively reflect the model's ' uncertainty, the batch-wise normalized entropy H (zrlip) is firstly computed follows:
polip Σ
Pi clip
(Galin)
= (1-
Σ
K
olip log log K; Pj
(p)lir))
(5)
where golip denotes the averaged prediction vector CLIP across a batch of size N. 13 denotes the j-th class probability of the averaged prediction vector polip, and K is the number of classes. By quantifying the model's ' predictive uncertainty through this approach, we utilize EMA to dynamically adjust the adaptation index t. During the early training phase, when higher learning rate is beneficial, d set to be greater than one to encourage exploration. As training progresses, the model requires more stable optimization, leading adaptation index vp to gradually decrease below one, with its variations being smoothly regulated:
" nmb + (1 n)) (p
where y acts as a momentum parameter that combines the previously computed & with the current H (gclip).
Tsallis mutual information (TMI) Subsequently, the standard MI loss (7) is replaced with Tsallis mutual information (TMI), where the adaptation index & dynamically adjusts the entropy behavior based on the model's uncertainty. To optimize the CLIP vision encoder, the TMI (prtip .p) between the CLIP prediction polip and the soft pseudo label ş is defined as follows:
In 1--
(p pp)ppp
Σ
K
PEK
In
PAR PrPw
where Pak denotes the joint probability the k-th class probability from CLIP prediction drl and the k'thh class from the soft pseudo label p. P& and Pe are the marginal probabilities, representing the probability vectors of polop and p. respectively. For details on how the joint and marginal probabilities computed for TMI, please refer to Appendix C
CLIP vision encoder optimization Tsallis mutual information (negative term) is used as a loss function to optimize the CLIP vision encoder, encouraging the model to maximize the mutual information between the CLIP prediction (p-lip) and the soft pseudo label (p). This formulated as:
LTMI = min T (p .P).
fv
By minimizing LTMI, the CLIP vision encoder is trained to enhance the alignment between the CLIP prediction pelip and the soft pseudo label p. while simultaneously adapting the adaptation index db to regulate the balance between exploration and exploitation during training.

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3.2 Iteration-level process
3.2.1 Pseudo label matching framework (PLMatch)
PLMatch, which belongs to the iteration level process effectively (1) performs supervised learning using hard pseudo labels, (2) leverages unlabeled samples through consistency training, and (3) conducts CLIP-guided knowledge distillation.
Pseudo supervision The target model is trained by minimizing the cross-entropy loss over the unlabeled set, using the hard pseudo label fi and the mask m, obtained from dual- sual-perspective pseudo labeling in CPG (Eq. T.. The corresponding formulation is as follows:
z =
= ===()=
1
K
(9)
Coup
ic, log(p',)) m4.
N
where a/ is the logit output of the target model for (I;)). N₁ is the number of pseudo labeled samples.
Consistency training In addition to pseudo supervision, we apply consistency training to all target domain samples encouraging the target model to learn more robust feature representations rather than being overly confident in misclassifying samples that have not been assigned pseudo labels. As illustrated in Fig. consistency training leverages both weakly augmented (a<(z;)) and strongly augmented (AAr;;) versions of the same image (x²). The training process is formulated as follows:
Leon E (=)r((((s))))))(((()))
(10)
N
where p² (a(x:)) and p' (A(xz)) represent the prediction vectors of (x;;) and A(x,) through the target model. K (--l--) denotes the Kullback-Leibler (KL) divergence between the two input distributions.
CLIP-guided knowledge distillation We further perform consistency training by minimizing the KL divergence between the prediction vectors from CLIP (prlip) and the target model (p'). The loss function for aligning the predictions of both models is formulated as follows:
CKL = N
(L(F(((z))lp
(11)
This alignment stabilizes the target model's 's feature representation, leading to more reliable predictions and improved generalization in the target domain.
Target model training The three previously described loss functions are utilized for training the target model. These loss functions are combined with different weighting factors to formulate the final loss function, as defined below:
Ltarget aLsup + BLon + 7LKL-
(12)
Here, a, 9, and y control the balance between losses, further described in the experiments.
4 Experiments
Datasets To evaluate the effectiveness of our proposed method, we conducted experiments on three widely used benchmark datasets for source-free domain adaptation: Office-Home(344 (34), VisDA-C(2] and DomainNet- 12([21] ( These datasets encompass diverse domains and category scales. Detailed descriptions of each dataset are provided in Appendix [
Implementation details For reproducibility and clarity, detailed implementation set- incl-incl backbone architectures, optimizer configurations, data augmentation strategies, and hyperparameters for each dataset-are thoroughly described in Appendix D.2

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Table 1: Accuracies (%) Office-Home dataset for ResNet50- baseed methods and VisDA-C for ResNet101- -based methods. "SF" denotes source-free free methods, while domain notation are as follows: A (Art), C (Clipart), R (Real-world), P (Product), Sy (Synthesis), and Re (Real). Bold text highlights improvements over prior methods.
Method
A-+C
A-R
P-+A
P--C
P-+R
R-+A
Avg. Sy
Source
43.7
67.0
73.9
49.9
60.1
62.5
51.7
40.9
72.6
64.2
46.3
78.1
59.2
49.1
SHOT
ICML20
55.0
78.8
81.3
69.1
79.1
79.0
68.0
54.8
81.8
73.6
58.9
83.5
71.9
82.2
NRC
NIPS2
53.8
76.6
78.6
73.9
73.1
61.9
52.2
77.5
70.3
69.8
82.1
IROS21
82.2
67.2
72.4
AdaCon
CVPR22
47.2
75.1
75.5
60.7
73.3
73.2
60.2
45.2
76.6
65.6
48.3
79.1
65.0
86.8
CoWA
JCML22
5644
79.2
80.9
68.6
78.6
75.8
68.0
55.9
82.5
69.0
65.8
79.9
68.6
84,3
SCLM
CVPR23
58.2
80.3
81.5
69.3
79.0
80.7
61.6
65.9
53.8
67.5
64.3
76.0
64.7
TPDS
UCC223
59.3
80.3
82.1
70.6
79.4
80.9
69.8
56.8
82.1
74,5
61.2
85.3
73.5
87.6
DIFO
CVPR24
90.6
82.5
80.9
70.1
88.9
83.4
83.1
90.0
Ours
73.6
90.4
91.0
83.6
90.7
90.9
82.7
73.7
91.2
83.6
74.0
91.2
88477
91.4
Table 2: Accuracies (%) on DomainNet-122 126 for methods with ResNet50. Domain indentation are as follows: C(Clipart), P(Painting), R(Real), and S(Sketch).
Method
Venue
SF
C-PP
C-+R
C-+5
P--C
P-+R
P-1
R-CC
R-P
R-
S-+C.
S->P
S-oR
Avg.
Source
44.6
59.8
47.5
53.3
75.3
46.2
55.3
62.7
46.4
55.1
50.7
59.5
54.7
SHOT
ICML20
63.5
78.2
59.5
67.9
81.3
61.7
67.7
67.6
57.8
70.2
64.0
78.0
68.1
NRC
NIPS21
62.6
77.1
58.3
62.9
81.3
60.7
64.7
69.4
58.7
69.4
65.8
78.7
67.5
GKD
IROS21
61.4
77.4
60.3
69.6
81.4
63.2
68.3
68.4
59.5
71.5
65.2
77.6
68.7
CVPR22
65.4
CoWA
ICML22
64.6
80.6
60.6
66.2
79.8
60.8
69.0
67.2
60.0
69.0
65.8
79.9
68.6
PLUTE
CVPR23
59.8
74.0
56.0
61.6
78.5
57.9
61.6
65.9
53,8
67.5
64.3
76.0
64.7
TPDS
UCV22
62.9
77.1
59.8
65.6
79.0
61.5
66.4
67.0
58.2
68.6
64.3
75.3
67.1
DIFO
CVPR24
76.6
87.2
74.9
80.0
87.4
75.6
80.8
77.3
75.5
80.5
76.7
87.3
80.0
Ours
80.1
89.6
79.0
82.4
89.8
79.2
82.8
80.6
78.8
83.0
80.3
89.6
82.9
Table 3: Unified evaluation of component configurations and loss weight ablation on Office-Home and VisDA-C datasets.
Component
Weights
OfficeHHome +P
VisDA-CC
Coup
Lown
DKL
7
++A
+R
Avg.
Sy-RR
0.4
0.2
77.0
68.3
87.2
86.4
79.7
0.4
0.2
0.4
82.9
73.2
90.8
90.9
84.4
91.0
0.4
0.2
0.4
83.3
73.8
90.8
91.0
84.7
91.4
0.5
0.1
0.4
83.2
73.5
90.8
90.9
84.6
91.4
0.5
0.2
0.3
82.7
73.4
90.9
90.9
84.5
91.4
0.4
0.1
0.5
83.2
73.4
90.8
91.0
54.6
91.4
0.3
0.4
0.3
83.0
73.2
90.8
90.7
84.4
91.2
Fine-tune
0.4
0.2
79.9
67.0
87.9
88.8
80.9
89.3
Fine-tune w/ MI
0.4
0.2
0.4
83.2
73.1
90.7
90.9
84.5
91.4
Fine- tume w/ TM1
0.4
0.2
0.4
83.3
73.8
90.8
91.0
84.7
91.4
4.1 Main results
As shown in Tabs. D and our method consistently surpasses prior state-of-the-art approaches across all benchmarks, with improvements of 1.6% on Office-Home, 1.4% on VisDA-C, and 3.0% on DomainNet- 126. The gains are especially notable in challenging domains such as Clipart and Sketch, where large domain gaps and stylistic differences hinder existing methods. These results highlight the robustness of our method in handling severe domain shifts and improving generalizable feature adaptation.
4.2 Contribution of investigated components
The ablation study presented in Tab. 3 evaluuate the effectiveness of the proposed components in our framework, incorporating results on the Office- Home and VisDA-C datasets. First, when using only the pseudo supervision loss (L.up) with hard pseudo labels, the proposed model achieves 79.2% average accuracy the Office-Home dataset, already surpassing existing source-free methods except 22 On VisDA-C, our model achieves 90. 00.2% accuracy, outperforming the state-of-the- method DIFO by +0.2%. This strong performance of pseudo supervision highlights the superiority of

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the proposed pseudo labeling strategy, and more detailed analysis of our pseudo labeling strategy is provided in Appendix D.3
Incorporating Leon and L boosts accuracy by 5.5% (84.7%), highlighting their role in enhancing adaptation stability. Additionally, optimizing the CLIP vision encoder with Tsallis mutual information (TMI) results in the best overall performance, outperforming both KL-based and mutual information (MI)-based approaches. Notably, TMI-based vision optimization yields a larger performance gain in the Clipart domain (+ C), indicating its effectiveness in mitigating domain shift when adapting to visually diverse domains.
4.3 Variations depending the loss weighting
We conducted an ablation study on the Office- and VisDA-C datasets to investigate the effect of weights B, and corresponding to L Luup Loon. and LKL. respectively. As shown in Tab. 3 the optimal configuration (a 0.4, 3 0.2, 7 = 0.4) achieved the best overall accuracy of 84.7% on Office-Home and 91.4% VisDA-C. Notably, the model maintains robust performance despite variations in loss weight settings. This setting also showed strong performance on + R (9100%),
but slightly reduced accuracy observed when 3 was lowered importance of consistency regularization and balanced loss combination.
increased, indicating the
4.4 Analysis of Tsallis mutual information
Figure 4 analyzes the optimization dynamics of Tsallis mutual information (TMI) and standard mutual informa- tion (MI) losses during domain adaptation from Art to Clipart in the Office-Home dataset. The top plot shows the adaptation index & decreasing over iterations. adap- tively adjusting the sharpness of the TMI. The bottom plot compares TMI and MI losses before and after the adaptation index y approaches one. The shaded regions in both plots divide the training process into two stages: the early stage encourages exploration, allowing the model to learn from diverse and uncertain samples, while the later stage favors exploitation, focusing on confident and stable predictions. As shown in Fig. TMI effectively regulates loss oscillations, likely due to its entropy index- controlled structure. The loss patterns indicate that TMI promotes an effective signing greater importance uncertain samples early in training. This structured learning improves model stability and makes DVO more robust solution for source-free do- main adaptation than standard MI- based training. The impact of TMM-Mbaaaaae CLIP vision optimization on our framework further analyzed in detail in AppendixD.3
Index
Exploration
Esploitation
-0.5
TMI Loss
Loss
Loss
0.27
TMI o
MI std: 0.13
MI std: 0.17
Iteraion
Figure Visualization of the adapta- tion index & (top) and the correspond- ing losses (bottom) for TMI and MI dur- ing adaptation from A-C in the Office- Home dataset.
balance, as-
4.5 Analysis of Pseudo label matching framework
In all cases, PLMatch reduces the gap between confidence and classification accuracy more effectively than DIFO. Even in challenging domain shifts like R to A, PLMatch maintains relatively low expected calibration error (ECE) (0.02) compared to DIFO (0.10). The superior calibration of PLMMacc can be attributed to its dual- persppeeseee pseudo labeling strategy, which prioritizes high-confidence samples initially and progressively refines pseudo labels over time. Additionally, consistency training enforces alignment between different augmented views, preventing the model from becoming overconfident in noisy pseudo labels. Additional results for other adaptation scenarios provided in Appendix D.5

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0.06
Oxp
Oup Accuracy
Oap Accunay
Gap
Exeeted Nesuae
Acaracy
Accuracy
Exyecta
0.0
000
1.00
8.00
0..00
(a) Ours (R-A)
(b) Ours (R-P)
(c) DIFO (R A))A)
(d) DIFO (R (R-P)
Figure 5: Reliability diagrams 19 comparing the proposed PLMatch and the existing state-of-the-ar
method, DIFO, on the Office-Home dataset. The source domain is Real-world (R), and the target
domains are Art (A) and Product (P).
Conclusion and limitations
To address pseudo label noise in source-free domain adaptation (SFDA) and facilitate Vil. model adap- tation to the target domain, we introduce a novel pseudo labeling strategy that integrates task-specific perspectives and domain-invariant characteristics, along with uncertainty-awarr optimization strategy for the CLIP vision encoder. By leveraging -omaann-enerralized CLIP predictions and task- specific model outputs, selectively assign high-quality pseudo labels. Additionally, consistency training refines feature representations for samples awaiting pseudo labeling, enhancing robustness. Our incertainty-based optimization approach balances exploration and exploitation, allowing the CLIP vision encoder to self-regulate its learning intensity. Empirical results demonstrate that our method outperforms prior approaches, particularly under challenging domain shifts with significant domain gaps. While our method requires optimizing the CLIP vision encoder, this introduces non- trivial computational overhead. Although we limit this process fewer than eight update steps in method, the added cost may still be a concern in snoource-contraingg settings, suggesting the need for lightweight adaptation strategies or parameter-offfciiet tuning methods in future work.
Acknowledgement
This research was supported by the National Research Foundation (NRF) funded by the Korean gov- emmment (MSIT) under Grant RS2023-00208763, and RS-2025-02653611 (Intelligent Semiconductor Technology Development Program).
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19

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Appendix
Table of Contents
A
21 Implementation detail of toy example
B
Effect of entropy index q on Tsallis entropy
.22
Calculation of joint probabilities
22
D
Experimental details
23
D.1
Benchmarks
23
D.2
Implementation details
23
D.3
Ablation details
23
D.4
Effectiveness of CLIP for target model training
25
D.5
Expanded calibration analysis
25
D.6
Feature distribution analysis
26
D.7
Confusion matrix comparison
28
D.8
28 Computational overhead
20

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A Implementation detail of toy example
Noise Level: 0,980, MI: -0,0084, TMI: 0.0468
0.60
Probability
0.45
GT
GT
GT Pred.
0.30
0.15
2
4
8
1011
2 3
10
2
6
8 10
Noise Level: 0.694, MI: -0.0089, TMI: 0.0157
0.60
Probability
0.45
GT
GT
GT
Pred.
0.30
0.15
2
4
8
1011
2
5
7
9 10
2
6
8
10
Noise Level: 0.388, MI: -0.0076,
-0.0051
0.60
Probability
0.45
GT Pred.
GT
GT Pred.
0.30
0.15
0.00
2
4
8 1011
2
5
7 9 10
6 8 10
Noise Level: 0.083, MI: -0.0255,
-0.0321
0.60
Probability
0.45
GT
GT
GT Pred.
Pred.
0.30
0.15
2
3
4
6
8
1011
2 3
5
10
2
6
8
10
Noise Level: 0.001, MI: -0.0504, -0.0586
0.60
Probability
0.45
GT
GT
GT
Pred.
Pred.
0.30
1011
2
1011
0 3
1011
Figure 6: Visualization of class probabilities from a toy example designed to analyze mutual infor-
mation (MI) sensitivity under different noise levels. The noise level refers the weight applied to Gaussian noise with mean () and standard deviation 1, which is added to the soft pseudo label to simulate prediction. For each noise level, three sample predictions are shown. Each subplot compares a soft pseudo label (purple) against its noisy version (orange). As the noise level decreases from top bottom, the noisy predictions become more similar the pseudo labels. The a-axis represents the class index.
We further investigate the limitations of using standard mutual information (MI) as optimization objective by designing a toy example to simulate the MI-based training process under decreasing uncertainty. As shown in Fig. vectors from a uniform distribution and then normalizing them via the softmax function to ensure they represent valid probability vectors. Controlled Gaussian noise is subsequently added these vectors to simulate noisy predictions. For each noise level, three random samples are visualized to illustrate the distributional shift. Each subplot displays pair of class probabilities: one representing the ground truth (pscudo label) and the other noisy version (prediction) of it. We use the soft label the ground truth to reflect the actual training setup, where mutual information (MI) computed based on the soft pseudo label. As the noise level gradually decreases (from top to bottom), the noisy predictions become more concentrated and closely aligned with the pseudo label. However, as discussed in ci)) despite the noticeable distributional improvements, the MI value remains relatively insensitive these changes. This demonstrates MI lacks the flexibility to dynamically capture information changes across varying prediction quality levels, particularly in uncertain early- stage adaptation scenarios. This observation supports the need for an alternative information measure like TMI, which dynamically modulates sensitivity through the proposed adaptation index v.. By
generate soft pseudo labels by randomly sampling class probability
21

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doing so, TMI provides more meaningful optimization signals during the both high-uncertaiin and stable training phases, leading to more robust adaptation performance.
B Effect of entropy index on Tsallis entropy
0.8
Dallis Fatrogy
0.6
0.1
0.5
g=0.8
4
0.2
q
Probability
Figure 7: Effect of the entropy index on the shape of Tsallis entropy. Each curve represents the Tsallis entropy computed over probabilities ranging from 0 to 1, under different settings.
To better understand how the entropy index g influences the behavior of Tsallis entropy B3 we visualize the entropy values over range of probabilities in Fig. 7 The shape of the entropy function varies significantly with entropy index When < 1, the entropy curve becomes sharper, increasing sensitivity high-confidence predictions and encouraging the model focus confident ples (exploitation). Conversely, when > 1, the curve becomes flatter, reducing the sensitivity to confidence and allowing the model explore uncertain predictions more freely (exploration). This property allows Tsallis entropy to flexibly control the trade-off between exploration and exploitation during training by simply adjusting the entropy index g. which is especially beneficial in domain adaptation scenarios with high uncertainty.
C
Calculation of joint probabilities
To compute the Tsallis mutual information (TMI) between CLIP's prediction (pelip) and the soft pscudo label (ő), we first construct a joint probability matrix P following the standard formulation adopted in prior w7 This matrix PRR captures how often each class prediction from CLIP co-occurs with each class prediction from the soft pseudo label, across all samples in mini-batch. Specifically, for each sample in the batch, calculate the outer product of the two probability vectors (p p) and then take the average across all samples. This gives us a matrix Pax E R K where K and N are the number of classes and samples, respectively. The calculation of matrix P is followed:
PRR
p lk
1 N
( P (6(9)T
(13)
N
The marginal probabilities Ps and Per are then obtained by summing this joint matrix PR across the rows and columns. The row sums represent the class probabilities from CLIP's prediction (pp),) and the column sums represent those from the soft pseudo labels (p). This is formulated as:
Pk Σ PER. P
Σ
Pa
(14)
The joint probabilities and marginal probabilities are then jointly used the computation of TMI, as formulated in Eq.
22

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D Experimental details
D.1 Benchmarks
To evaluate the source-free domain adaptation performance of our proposed method, we used three benchmark datasets: Office-Home[Be VisDA-C(22), and DomainNet-126f22 Office-Home comprises 15,500 images across four domains (Art, Clipart, Product, and Real-world), each with 65 categories. VisDA-C large-scale simulation-to-real dataset with over 152k images spanning 12 categories, where training images are synthetically generated, and validation images are real-world samples from MS-COCO DomainNet- 126 is large-scale dataset derived from DomainNet. As refined subset, DomainNet- 126 contains 145k images from 126 classes, samples from four domains (Clipart, Painting, Real, and Sketch).
D.2 Implementation details
Table slyper-paameed settings of the proposed method. π) and fγ represent the learning rates of the target model and the CLIP vision encoder, respectively.
Hypar-parameters
Office-Home
VisDA-C
DomainNet-126
Ratch size
64
Optimizer
SGD
SGD
Momentum
0.9
0.9
0.9
Weight-decny
0,001
0.001
0.001
7
0.01
0.001
0.001
Cycle
Iteration
4
Adaptation index g
1,05
1,05
1.1
Momentum Ja
0.99
1o-7
1o-7
Io-7
We employed 16) as the feature encoder for both the pre-trained source model and the target model on the Office- Home and DomainNet-1 126 datasets, while ResNet101 [37] was used for VisDA- C. The target model was trained using stochastic gradient descent (SGD) as the optimizer with a momentum of 0.9 and weight decay of 0.001. The learning rate set to 0.01 for Office-Home and 0.001 for both VisDA-C and DomainNet-125 optimized using the Adam optimizer with fixed learning rate of 10-7 For consistency training, weak augmentation involves random cropping and horizontal flipping. while strong augmentation included randomly sized cropping, Gaussian blurring, and horizontal flipping to introduce greater variability. In terms of training schedule, the cycle was set to 4 for Office-Home and DomainNet- 126, and for VisDA-C, with each cycle comprising iterations. The entropy index q and momentum μ were adjusted slightly for each dataset to stabilize training. The weights a, B, and of loss functions were set 0.4, 0.2, and 0.4 for all datasets, respectively. All experiments were conducted on an NVIDIA GeForce RTX 3090 GPU,
In contrast, CLIP's vision encoder (ViT/B-32 was
D.3
Ablation details
D.3.1
Comparison between Tsallis mutual information (TMI) and mutual information (MI)
To examine the effectiveness and robustness of the proposed Tsallis mutual information (TMI), we conducted a comparison between mutual information (MJ) and TMI across different random seeds, and additionally evaluated performance when replacing the backbone of the CLIP vision encoder. As shown in Tab, 5 the performance advantage of TMI over MI consistently appeared. Notably, the performance gap more pronounced when using lightweight backbones such ResNet [6 37 variants, compared to the ViT-B/32 used in the final model.
Moreover, CLIP in our framework contributed in two major ways: knowledge distillation and pseudo label refinement. To assess TMI's contribution in these areas, we measured Expected Calibration Error (ECE) of CLIP, CLIP accuracy, and pseudo label accuracy on tasks where the performance improvement was most pronounced. As shown Tab. [6 the results suggested that CLIP trained with TMI achieved more stable and well-calibrated learning state than with MI, which positively affected the quality of the pseudo labels. These findings indicate that TMI-based training can be especially useful in various tasks.
23

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Table 5: Comparison of mutual information (MI) and Tsallis mutual information (TMI) the Effice-Home dataset. For ViT-B/32, results are averaged over multiple random seeds, while RN50 and RNIO1 rows show performance when replacing the CLIP vision encoder backbone.
Backbone Method
A-C
A-P
A-R
C-+A
C-+
C-R
P-+A
P--
P-+R
R-+A
R-C
R-P
Avg.
73.07
90.47
90.87
83.27
87.12
90.93
82.70
90.73
91.00
83.70
73.17
91.13
84.47
VTT-B332
Ours (w/MI)
(::015)
(+021)
(40.10)
(=0.06)
(#0.17)
(40.12)
(4008)
(20.12)
(#0.17)
(#0032)
(4:0.52)
(+006)
(2:012)
73.27
90.43
90.87
83.43
90.73
90.83
83.00
90.73
91.20
83.73
73.77
91.20
84.63
Ours (w/IMI)
(1:0.31)
(1:0.66
(2:0.12
(2:0.15
(1:0.06)
(1:0.26)
(1:0226
(2:0.35)
(2:0.15
(1:0.25)
(1:0225)
(1:0.00
(1:0.08
RN50
Ours (w/M)
59.0
84,8
84.6
78.7
85.5
86.8
78.7
60.3
87.2 2
78.7
60.4
85.2
77.6
Ours (w/TMI)
60.5
84.9
86.9
78.5
85.7
86.7
79.0
61.6
86.9
78.7
61.5
85.2
78.0
RNIOI
Ours (w/M))
63.9
89.1
88.1
88.5
82.5
64.6
89.1
89.0
82.7
63.0
89.0
Ours (w/TM))
64.2
89.3
88.8
82.8
89.7
88.8
81.7
65.2
89.0
82.7
63.3
89.7
81.4
Table 6: Comparison of MI and TMI in Dynamic entropy-guided vision optimization (DVO) across different tasks. (ECE: Expected Calibration Error, CLIP Acc.: CLIP accuracy, PL. Acc.: Pseudo label accuracy.)
Task
Loss
DVO BCE(L)
CLIP Acc.(f)
PL (cc.(1)
A-+C
MI TMI
0.20
77.0 77.4
71.8
0.18
72.2
P-C MI
0.20
76.9
71.6
TMI
0.18
77.7
72.4
R-C MI
0.19
77.6
71.6
TMI
0.17
77.7
72.4
D.3.2 Analysis on pseudo label
Table 7: Pseudo label accuracy(5)) (%) and its sampling ratio on Office-Home and 1omainNet-126
Dataset
Scenario
Cycle
Cycle 2
Cycle 3
Cyele 4
A-+C
(179(1172/4300))
81 (3.5000/1//553000
79. (3,044(3369/4338)
77, 17.48% 77.48%(3948/4444 (3948/4365)
A-P
94.20% (2913/1139)
92.62% (1997/733311333
(2.52%(12337411
92.03% (1315/1139)
A-R
95.74%;310) 4357)
94.13% (3835/4337))
93.35%((477//44
92.71 (4182/44357)
C-AA
94.68%
(242)
90.79% (727)
88. (8.666%(2)) 107 /2427)
87.57% (221 3/22277)
C-P
94.75%
(2631/1139)
(-19) 93.44%
92.46%((4222)
92.17% (1306/1439)
Office-Home
C-R
95.72% 4357)
94.08% /4357)
93. 93.29% (4000/44740 (4357)
92.49% ( (4196/4357)
P-AA
93.47% (1 (2427)
90.55% 562 /2427)
89.89% (2089) (2427)
87.29% (2203/2427)
P-C
87.85% (160// 4365)
81 (3.56%11111 (31 18/ (8/3365)
79.44% /43655)
78.22% (3930) (930043365)
P-R
95.85% (301.] 1/1357)
94.02%(387 (1357)
93.275 (108-)))88))) (11357)
92.77% (1190/1357))
R-+A
93.58% (1449) (2427)
90.71% 9226 (2427)
(8191 19/11999/// (2427)
87. 62.62% (2213, 3/427)
R-C
84.60%[[91) (1966)99916///// 111199664669 4365)
00.92%%(319)) /4365)
(3790 (4365)
77.91% (3965/
R-P
94.44% (3382/1139)
92.95% (1038/419)))
(2.54%(
92. 18. (3382/ 1439)
Avg.
92.7
89.8
88.4
87.5
C->P
92.97%
(12638/300 /30042)
90. 10% (22620/30042)
88. 64% (24035/3000 /30042)
87.49 97.43% (28057 038057/300422
C-R
96.31%
(40130/6962) (6622)
94.26% (00876/60612))
93. 26%
(64080/69622)
92.40%
(65707 666222
C-8
92.76% 10306/2400 47)
89.05% (7734/24147)
87. 48% (19818 (0081//21447
85.97%
(20030/ 241477
P--C
93.01 % (9258/ 18523))
90.04% (14455/18855 (1522))
36. (15791 (18523)
87.33%
(16530/ 18223)
P-
96%16%
(50664/ /66622)
94. 19% (61 S01 (69622)
93. 25% (6457) /60622)
02. 12..6%%
(66034 (60622)
DomainNet-126
P-
93.70% (1042//0 (24147)
89.30% (17924/241) /241447
67.38%
(19880 (24147)
85.90%
(21002/2 221147)
R-C
92.88% (9623/ / 18523)
89.87%
(14566/ (4566/18523)
88. 33% (15875/ 18523)
87.18%
(16569/16 (15533)
R-
93.18%
(1 17833/ /300422)
80.00 80.93%
(23479/33004
88. 30% (25538/30 /30042))
87.07%
(26394/30042
R-S
95.85%
(10886/241447 (24447)
94.02%
(17928/ (24147)
93.275 93.27% (19943/22 221477)
92.77%
(21015/24147
S-CC
90.80% (9235/ 18523)
87.95 87.98%
(14436/188) (18523))
86. 48% (15797/ 118523)
85.35%
(16506/ / (1823)
S-P
95.18%
(14221 (30042)
90.68% (228 15/30442)
80% (24901 /30042)
87.53% (26072/ /30042)
S-R
96.07% (407 13/ 136662
97.17%
(09622)
91.03% (641177/606))
92.36% (18777/66662)
Avg.
94.1
91.1
89.6
88.6
We conducted quantitative analysis of pseudo label accuracy and sampling ratio per cycle on the Office-Home dataset [34]. As shown in Tab. 7 % values indicate pseudo label accuracy, and values parentheses show the sampling ratio. Pseudo label accuracy declined across cycles (92.7% 89.8%, 88.4%, 87.5% as more pseudo labels introduced inevitable noise. However, prior work [8, 40) shows that such noise can improve generalization capability. This trade-off between pseudo label accuracy and sampling ratio is analyzed in detail in the following section.
24

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D.3.3
Trade-off between accuracy and the number of pseudo labels
Table 8: Comparison of pseudo label accuracy(% and sampling ratio across cycles for different confidence thresholding (CT) strategies and our proposed pseudo labeling method on the Office-Homi dataset.
Threshold
Scenario
Cycle
Cycle 2
Cycle
Cycle 4
0.85
P-C
98.82% (0.03)
97. 19% (0.20)
94, 72% (0.37)
91.07% (0.47) 97.88% (0.52)
R R-+
99.18% (0.15)
98.67% (0.34)
98.09% (0.45)
0.95
P-C
100% (0.007)
98. 79% (0.09)
97.4 47.41% (0.19)
70% (0.28)
R-+A
(0.04)
99.54% (0.18)
99.39% (0.27)
99.49% (0.32)
0.85 0.95
P-C
98.82% (0.04)
98. 38% (0.18)
96.49% (0.29)
96. 91% (0.34)
R A
99.18% (0.15)
98.64% (0.30)
98.67% (0.37)
98.75% (0.40)
Ours
P-C
87.85% (0.37)
81.56% (0.71)
79.44%
(0.84)
78.22% (0.90)
R +
93.58% (0.60) 90.71% (0.79) 88.91% (0.87) 87.62% (0.91)
Table 9: Comparison of target model classification performance between different CT strategies and our proposed method on the Office-Hooe dataset.
Threshold
A-+C
A-PP A-RR C-+A
C-+P
C-+R
P-+A
P-4C
P-+ R-+
R-
R++P Avg.
0.85
73.1
90.6
82,9
90.5
90.8
82.4
72.8
91.0
83.8
73.8
90.9
84,4
0.95
72.9
90.5
90.8
82.7
90.4
90.6
82.3
73.0
90.8
83.7
73.5
91.0
84.3
0.85 to 0.95
72.9
90.4
90.7
82.8
90.5
90.6
82.3
72.7
90.9
83.6
73.6
91.0
84.3
Ours
73.6
90.4
91.0
83.6
90.7
90.9
82.7
73.7
91.2
83.6
74.0
91.2
84.7
The drop in pseudo-label accuracy during later cycles is attributed to the increasing number of assigned pseudo labels, which highlights natural trade-off between label quality and coverage. To further analyze this trade-off, we conducted additional experiments on the Office-Home dataset using confidence thresholding (CT) to assign pseudo labels, controlling the balance between pseudo label accuracy and quantity. As shown in Tab, S CT-based methods produced high-quality pseudo labels but labeled fewer samples than ours. In contrast, our approach achieved better overall performance This aligns with the training strategy[2 gradually incor- porating lower confidence labels. This enables broader learning and mitigates overfitting, suggesting that our method ensures practical and stable label quality without complex thresholds or strategies.
and more stable training shown Tab. g
D.4 Effectiveness of CLIP for target model training
Table 10: Comparison of CLIP and the final target model on the Office- Hiiee--Hmeeee dataset. Each row reports the performance of the model indicated in the first Model column.
Model Setting
A-+C
A-+P
A-R
C-AA
C-+
C-RR
P-
P-+C
P-R
R-*A
R-C
R-+P
Avg.
CLIP
w/o tuning
60.1
84,2
85,8
75.2
84.2
85,8
75.2
60.1
85.8
75.2
60.1
84.2
76.3
tuning
72.2
89.8
90.1
82.2
89.9
90.1
81.6
72.4
90.3
82.2
72.4
90.0
83.6
Target wlo CL.IPtuning
65.8
87.9
89.2
80.7
88.
89.1
80.4
66.4
89.0
81.1
66.4
88.3
81.0
CLIP Juning
73.6
90.4
91.0
83.6
90.7
90.9
82.7
73.7
91.2
83.6
74.0
91.2
84.7
The primary objective of the proposed framework is to train the target model, not directly use CLIP as the final model. The purpose of CLIP fine-tuning is therefore not to replace the target model, but rather to help the target model training with pseudo label refinement and knowledge distillation.
As shown in Tab. 10 the final target model (Proposed) outperformed both the frozen (CLIP w/o tuning) and fine-tuned CLIP models (CLIP w/ tuning), confirming that CLIP is best used as an auxiliary module rather than the main predictor.
D.5 Expanded calibration analysis
As shown in Fig. g ML consistently achieved a lower expected calibration error (ECE)
compared to DIFO 29 across various domain adaptation scenarios, indicating better alignment between confidence and accuracy.
25

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6.75
Accericg
0.75
0.75
0.50
0.25
0:24
0.2
600
0.900
0.g
1,00
Coufdesse
Couldrure
(6) DIFO (A-C) ECT
(b) DIFO (C-+A) DCE
(c) DIFO AP)
(4) DIFO (R-+A)
1.0
Gay
Oup
0.75
Accuracy
Accumscy
Actuncy
0.75
Accericy
0.50
0:50
0.59
0.25
0:25
0:25
6.00
6.00
0.25
0.50
0.75
1.00
0.0
60
0.25
0.59
0.25
100
0.5g
0.90
0.75
0.35
1.00
(c) DIFO (A-P)
(1) DIFO (C-P)
(g) DIFO (P-C)
(b) DIFO (R-C)
110
ECE 0:42
110
BCE- 6:42
1000
ICB 0.50
100
DCB EUM)
Gop
Cap
Accurpy
Accanacy
Accinity
Aucrncy
0.50
0.50
0.50
0:50
0.00
0.00
0:25
5.50
0.75
1.00
0.00
6.0
0:25
0.50
0.75
1.00
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Figure& 8: Calibration plots comparing DIFO and PLMatch across various domain adaptation scenarios on the Office- Home dataset. Each plot illustrates the expected accuracy versus confidence, where the gap represents the miscalibration.
D.6 Feature distribution analysis
Figure represents the t-SNE visualizations of the target domain features produced by various adaptation methods. Compared to existing approaches such as SHOT[12,, GKD(28), NRCB88) and 29), our method forms more compact and well-separated clusters with clearer boundaries.
26

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(a) SHOT
(b) GKD
(0) NRC
(d) COWA
PLUE
(0 TPDS
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(h) Ours
Figure 9: The t-SNE visualization of target domain features extracted by different domain adaptation methods on the VisDA-C dataset.
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Figure 10: Comparison of class-wise prediction results across different SFDA methods on the VisDA- C dataset. Each matrix visualizes the classification accuracy between predicted (columns) and true (rows) labels. Darker diagonal lines indicate higher classification accuracy.
27

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D.7
Confusion matrix comparison
Figure 10 presents the confusion matrices of various SFDA methods, including SHOT GKD 3RC COWA[I]] PLUE[[3), TPDS[27], DIFO[29) and our proposed method on the VisDA- C22 dataset. Each matrix illustrates the classification performance across all 12 classes, where diagonal elements indicate correctly predicted samples. Our method shows a clearly dominant diagonal pattern with significantly fewer off-diagonal elements compared other approaches, indicating stronger class-wise discrimination and fewer misclassifications This indicates the superior feature alignment and inter-class discrimination.
D.8 Computational overhead
Table 11: Comparison Computational Cost among SFDA Methods
Method
TTB (9)
IT (ms)
GPU memory (GB)
SHOT
0,3621
0.608
7.759
DIFO
0.6576
8.340
To provide clearer analysis of computational limitations, report training time per batch (TTB), inference time (IT) per sample and GPU memory usage for the Crtpprl task from the Office- Home dataset 34] Although our method requires higher GPU memory due to using both CLIP and the target model, its hierarchical structure with separate cycle- and iteration-level processes processes-emaleee enables faster training than DIFO [29], which lacks such decomposition. Inference time per sample is identical across methods, as it depends only on the target model.
28


Reviews:
[{"review_id": "7cjjsEto8Z", "reviewer": "Reviewer_NoPH", "summary": "This paper addresses the problem that prior methods in Source-Free Domain Adaptation (SFDA) rely on inaccurate pseudo-labels in the early stages of training, and the limited task-specific adaptability of CLIP-based prompt tuning due to its frozen vision encoder, which results in low-quality image representations. To solve this problem, the authors propose assigning pseudo labels only to samples where the target model and the CLIP model agree, while utilizing consistency learning for the remaining samples to enhance feature representation. Additionally, they propose an uncertainty-aware optimization strategy based on Tsallis mutual information (TMI) that dynamically aligns the CLIP vision encoder to the target by balancing exploration and exploitation. The proposed method outperforms existing baselines on standard SFDA benchmarks.", "questions": "1. It would be helpful if the Related Work section included a dedicated paragraph on SFDA\n2. In Figure 1, why does the pseudo-label accuracy of the proposed method drop in later iterations? Is it because the number of assigned pseudo labels increases? If so, could the trade-off between accuracy and the number of pseudo labels assigned be controlled? And under what conditions would this be most efficient?\n3. In Figure 3, specifically in the CPG part, what is the reason for the varying bar widths?\n4. Why was the averaging factor set to 2 in the prediction mixture? Have you experimented with other weighting schemes?\n5. The paper states that consistency training is applied only to the remaining samples, which are not assigned pseudo labels. However, Equation (10) computes the loss over all N samples. Shouldn't it be restricted to the unlabeled subset? This discrepancy needs clarification.", "limitations": "The limitations of the proposed method are described in Section 5 (Conclusion and Limitations).", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\n1. This paper clearly highlights the limitations of prior methods and motivates the proposed approach through insightful experiments, particularly by visualizing the weaknesses of DIFO and the frozen CLIP encoder in Figures 1 and 2.\n\nWeaknesses\n1. The descriptions in the Introduction and Method sections are not well aligned, which hinders comprehension. While the Introduction briefly outlines only Dual-facet pseudo labeling and TMI-based vision optimization, the Method section introduces several new components—such as the Cycle-level process / Iteration-level process framework, CPG, DVO, and PLMatch—without prior context. As a result, it is unclear whether these components represent core contributions or simply implementation details.\n\n2. As shown in Table 3, the performance gain of TMI, which is one of the main contributions of the paper, is marginal compared to the standard MI. Considering that TMI introduces additional computational cost (e.g., the calculation of ψ), the benefit of using TMI may not be significant in practice.\n\n3. Both TMI in the Cycle-level process and CLIP-guided knowledge distillation in the Iteration-level process appear to align the CLIP model with the target model. However, only TMI incorporates adaptation uncertainty (ψ), while CL does not. The paper lacks a clear explanation as to why uncertainty modeling is applied to one but not the other, and further clarification on the design intention behind this asymmetry would be helpful.", "was_revised": true, "final_justification": "The concerns I raised were adequately addressed by further clarification and experimentation through the authors’ rebuttal. I particularly appreciate the responses to my two main concerns:\n\n1) While the performance gains with TMI are marginal, I acknowledge that it improves various aspects, such as calibration and label quality.\n\n2) The explanation of the asymmetric use of uncertainty modeling in the alignment mechanism clarifies the design intent and rationale for the framework.\n\nFinally, this paper presents a well-structured motivation and a corresponding method. However, the performance gain of the core method, TMI, is very marginal, and issues such as the figure mentioned in Q3 and the inconsistency between the equation and description noted in Q5 raise concerns about the overall quality of the paper. Therefore, I have decided to assign a borderline accept.", "rebuttal": "**(W1) Implementation details and core contributions**\n\nWe sincerely apologize for the confusion caused by the misalignment between the Introduction and Method sections. \nWe will clearly distinguish between the implementation details and core contributions as follows:\n\n| Implementation details | Core contributions |\n|-|-|\n|Cycle-level process|CPG, DVO|\n|Iteration-level process|PLMatch|\n\nSpecifically, the concepts of the cycle-level and iteration-level processes are introduced to clearly articulate the hierarchical structure of our core contributions. In the cycle-level process, computationally intensive components such as the proposed CPG and DVO are executed. In contrast, the iteration-level process applies to the relatively lightweight proposed PLMatch module.\n\n---\n**(W2) Marginal performance gain of TMI compared to MI**\n\nWe acknowledge that the comparison of the performance gain between TMI and MI reported in Table 3 may appear marginal. To address this concern, we conducted an ablation study comparing MI and TMI using three different seeds. (For transparency, we note that the TMI results are identical to those used in the statistical rigor analysis in **Reviewer EPpg's W1**.)\nAdditionally, following prior work [27], we compared the performances of the proposed method with MI and TMI when the CLIP vision encoder is replaced with ResNet-50 and ResNet-101 as backbones.\n\nAs shown in **Table A**, the performance advantage of TMI over MI consistently appears, confirming the findings in Table 3 of the main text. Notably, the performance gap is more pronounced when using lightweight backbones such as ResNet variants, compared to the ViT-B/32 used in the final model. This suggests that TMI provides greater performance gains over MI in scenarios involving lightweight architectures.\n\n**Table A.** An ablation study on MI and TMI using the Office-Home dataset.\n|Backbone|Method|A→C|A→P|A→R|C→A|C→P|C→R|P→A|P→C|P→R|R→A|R→C|R→P|Avg.|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|ViT-B/32|Ours (w/MI)|73.07 ± 0.15|**90.47 ± 0.21**|90.80 ± 0.1|83.27 ± 0.06|90.50 ± 0.17|**90.93 ± 0.12**|82.70 ± 0.0|73.03 ± 0.12|**91.00 ± 0.17**|83.70 ± 0.1|73.17 ± 0.32|91.13 ± 0.06|84.47 ± 0.12|\n||Ours (w/TMI)|**73.27 ± 0.31**|90.43 ± 0.06|**90.87 ± 0.12**|**83.43 ± 0.15**|**90.73 ± 0.06**|90.83 ± 0.06|**83.00 ± 0.26**|**73.33 ± 0.35**|90.93 ± 0.25|**83.73 ± 0.15**|**73.77 ± 0.25**|**91.20 ± 0.10**|**84.63 ± 0.08**|\n|RN50|Ours (w/MI)|59.0|84.8|86.4|**78.7**|85.5|**86.8**|78.7|60.3|**87.2**|**78.7**|60.4|**85.2**|77.6|\n| |Ours (w/TMI)|**60.5**|**84.9**|**86.9**|78.5|**85.7**|86.7|**79.0**|**61.6**|86.9|**78.7**|**61.5**|**85.2**|**78.0**|\n|RN101|Ours (w/MI)|63.9|89.1|88.4|82.1|88.9|88.5|**82.5**|64.6|**89.1**|**82.7**|63.6|89.0|81.0|\n| |Ours (w/TMI)|**64.2**|**89.3**|**88.8**|**82.8**|**89.7**|**88.8**|81.7|**65.2**|89.0|**82.7**|**65.3**|**89.7**|**81.4**|\n\nMoreover, CLIP in our framework contributes in two major ways: knowledge distillation and pseudo label refinement. To assess TMI's contribution in these areas, we measured Expected Calibration Error (ECE) of CLIP, CLIP accuracy (CLIP Acc.), and pseudo label accuracy (PL Acc.). As shown in **Table B**, the results suggest that CLIP trained with TMI achieves a more stable and well-calibrated learning state than with MI, which positively affects the quality of the pseudo labels. These findings indicate that TMI-based training can be especially useful in scenarios with large domain gaps. \n\n**Table B.** Analysis of fine-tuned CLIP with MI and TMI.\n\n|Task|Loss in DVO|ECE(↓)|CLIP Acc.(↑)|PL Acc.(↑)|\n|-|-|-|-|-|\n|A→C|MI|0.20|77.0|71.8|\n||TMI|**0.18**|**77.4**|**72.2**|\n|P→C|MI|0.20|76.9|71.6|\n||TMI|**0.18**|**77.7**|**72.4**|\n|R→C|MI|0.19|77.6|71.6|\n||TMI|**0.17**|**77.7**|**72.4**|\n---\n**(W3) Asymmetric use of uncertainty modeling in alignment mechanisms**\n\nTo clarify our design intentions, we provide the following explanation:\n\nAt the cycle level, DVO with TMI encourages informed updates through uncertainty-driven exploration, resulting in slower but more deliberate convergence. In contrast, the iteration-level process focuses on rapid convergence by leveraging confident pseudo-labeled samples, avoiding uncertainty-based strategies and instead adopting knowledge distillation.\n\nTo assess the impact of this design, we extended TMI-based objectives to the iteration level. As shown in **Table C**, our full design (Asymmetry) consistently outperforms the TMI-only variant (Symmetry), demonstrating its effectiveness.\n\n**Table C.** Comparison between symmetric and the proposed asymmetric approach on the Office-Home dataset.\n\n| Settings |Objectives in cycle-level|Objectives in iteration-level|→A|→C|→P|→R|Avg.|\n|-|-|-|-|-|-|-|-|\n|Symmetry|TMI|TMI|78.1|69.1|86.9|86.9|80.3|\n|Asymmetry (Proposed)|TMI|KL|**83.3**|**73.8**|**90.8**|**91.0**|**84.7**|\n\n---\n**(Q1) Lack of dedicated discussion on SFDA in Related Work**\n\nThank you for your valuable suggestion. We will add a new subsection to briefly introduce representative prior works that inspired our method—such as [11], [1], [27]—to help readers better understand the motivation and positioning of our approach.\n\n---\n**(Q2) Trade-off between accuracy and the number of pseudo labels**\n\nWe appreciate your observation. As correctly pointed out in your question, the drop in pseudo-label accuracy during later iterations is indeed attributed to the increasing number of assigned pseudo labels (PLs), which highlights a natural trade-off between label quality and coverage. To further analyze this trade-off, we conducted additional experiments on the Office-Home dataset [32] using confidence thresholding (CT) to assign PLs, controlling the balance between PL accuracy and quantity.\n\n**Table D.** Comparison of PL accuracy and sampling ratio across cycles for different CT strategies and our proposed method on the Office-Home dataset.\n|Threshold|Scenario|Cycle 1|Cycle 2|Cycle 3|Cycle 4|\n|-|-|-|-|-|-|\n|0.85|P→C|98.82%(0.03)|97.19%(0.20)|94.72%(0.37)|93.07%(0.47)|\n||R→A|99.18%(0.15)|98.67%(0.34)|98.09%(0.45)|97.88%(0.52)|\n|0.95|P→C|100%(0.007)|98.79%(0.09)|97.41%(0.19)|96.70%(0.28)|\n||R→A|99.03%(0.04)|99.54%(0.18)|99.39%(0.27)|99.49%(0.32)|\n|0.85 to 0.95|P→C|98.82%(0.04)|98.32%(0.18)|96.49%(0.29)|96.11%(0.34)|\n||R→A|99.18%(0.15)|98.64%(0.30)|98.67%(0.37)|98.75%(0.40)|\n|Ours|P→C|87.85%(0.37)|81.56%(0.71)|79.44%(0.84)|78.22%(0.90)|\n||R→A|93.58%(0.60)|90.71%(0.79)|88.91%(0.87)|87.62%(0.91)|\n\n**Table E.** Comparison of classification performance between different CT strategies and the proposed method on the Office-Home dataset.\n|Threshold|A→C|A→P|A→R|C→A|C→P|C→R|P→A|P→C|P→R|R→A|R→C|R→P|Avg.|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|0.85|73.1|90.3|90.6|82.9|90.5|90.8|82.4|72.8|91.0|**83.8**|73.8|90.9|84.4|\n|0.95|72.9|**90.5**|90.8|82.7|90.4|90.6|82.3|73.0|90.8|83.7|73.5|91.0|84.3|\n|0.85 to 0.95|72.9|90.4|90.7|82.8|90.5|90.6|82.3|72.7|90.9|83.6|73.6|91.0|84.3|\n|Ours|**73.6**|90.4|**91.0**|**83.6**|**90.7**|**90.9**|**82.7**|**73.7**|**91.2**| 83.6|**74.0**|**91.2**|**84.7**|\n\nThe rows labeled “0.85” and “0.95” in **Table D** correspond to settings where a fixed CT of 0.85 or 0.95 was applied throughout training. \n\nThe row “0.85 to 0.95” indicates a linearly increasing threshold, designed to progressively raise the confidence threshold in order to maintain PL accuracy across cycles. \n\nAs shown in **Table D**, CT-based methods produce high-quality pseudo labels but label fewer samples than ours. In contrast, our approach achieves better overall performance and more stable training (**Table E**). This aligns with the training strategy [a] of gradually incorporating lower confidence labels. This enables broader learning and mitigates overfitting, suggesting that our method ensures practical and stable label quality without complex thresholds or strategies.\n\n---\n**(Q3) Figure modification**\n\nWe sincerely apologize for any confusion in Figure  3. Although it was initially intended to emphasize class separability along with color, we will unify the bar widths in the final version.\n\n---\n**(Q4) Justification for averaging factor and weighting schemes in prediction mixture**\n\nThank you for your insightful question. We empirically set the averaging factor used in the prediction mixture to 2 after experimenting with various schemes.\n\n**Table F.** Ablation study on different weighting schemes used for the prediction mixture on the Office-Home dataset.\n|Scheme|**α**|**(1 - α)**|**→A**|**→C**|**→P**|**→R**|**Avg.** |\n|-|-|-|-|-|-|-|-|\n|Fixed|0.1|0.9|82.5|73.6|90.6|90.7|84.6|\n|-|0.3|0.7|83.0|73.4|90.7|90.8|84.5|\n|-|0.5|0.5|83.3|**73.8**|**90.8**|**91.0**|**84.7**|\n|-|0.7|0.3|**83.4**|73.5|90.7|90.8|84.6|\n|-|0.9|0.1|83.3|72.7|90.6|90.7|84.3|\n|Dynamic|Linear||83.0|73.2|90.4|90.8|84.3|\n|-|Random||83.1|73.0|90.6|90.9|84.4|\n|-|Exponential||82.9|73.1|90.4|90.8|84.3|\n\nWe conducted experiments using both fixed and dynamic balancing schemes for the prediction mixture. In this context, we denote α as the weight assigned to the target model's prediction, and (1 − α) as the weight for CLIP's prediction. Additionally, we explored the following dynamic strategies:\n\nLinear and Exponential: α increases linearly or exponentially over cycles\n\nRandom: α is sampled randomly from a uniform distribution\n\nThrough these experiments, we found that the fixed averaging factor (α = 0.5) provided a simple yet effective solution and served as a strong baseline throughout our work.\n\n---\n**(Q5) Inconsistency between textual description and Equation (10) regarding consistency training**\n\nWe sincerely thank the reviewer for pointing out the discrepancy between Eq. (10) and the description in Lines 240–242. As correctly reflected in Eq. (10), we apply consistency training to all samples regardless of their pseudo-label assignment. We will revise the sentence in the final version to more accurately reflect our implementation and design intent.\n\n---\n**References**\n\n[a] Tri-Net for Semi-Supervised Deep Learning. Dong-Dong Chen et al. IJCAI 2018."}, {"review_id": "agIp5N5lUh", "reviewer": "Reviewer_PbfW", "summary": "This paper introduces a two-stage, uncertainty-aware source-free domain adaptation (SFDA) method by leveraging an external, pre-trained vision-language model, CLIP. The authors alternate between fine-tuning the CLIP vision encoder and adapting the source model, incorporating Tsallis mutual information (TMI) to guide optimization. The proposed framework demonstrates improved SFDA performance and enhanced model calibration.", "questions": "1. Since the TMI loss is applied exclusively to the CLIP vision encoder, I am curious whether and how the improvements in CLIP representations contribute to the final adaptation performance of the target model. Could the authors provide further analysis on this aspect? Besides, is there any performance comparison between the fine-tuned CLIP encoder and the final adapted target model on the target domain?\n    \n2. What is the quality of the filtered hard pseudo labels? Would incorporating the mixed soft prediction $\\hat{p}$ into the PLMatch framework further improve adaptation performance?\n    \n3. The authors mentioned the computational limitations of the proposed method. Are there any runtime or resource usage statistics to support its practical applicability?\n    \n4. Some notations and terminology can be clarified.\n    \n    - For instance, in Line 210, what does \"EMA\" stand for?\n        \n    - Also, the formulation of the TMI loss in Eq. (8) appears incorrect. The \"min\" operator may be confusing or misplaced, as the negative TMI is already used as the optimization objective.", "limitations": "The authors have mentioned their limitations in the Conclusion and limitations Section.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### **Strengths**\n\n- **Novelty**  \n  The use of Tsallis mutual information for uncertainty-aware training captures model dynamics during adaptation. This approach appears novel and may offer valuable insights to the community.\n\n- **Empirical Effectiveness**  \n  The proposed method improves performance on SFDA benchmarks. The experimental results further demonstrate that the adapted model is better calibrated under the uncertainty-aware training paradigm.\n\n### **Weaknesses**\n\n- **Insufficient Experimental Analysis**  \n  - The computational efficiency and scalability of the method are not sufficiently discussed. This raises concerns about the practical value of the proposed approach. Please refer to **Question 3** for more details.  \n  - There is no direct comparison with some recent state-of-the-art methods that also leverage pre-trained foundation models, such as [1].  \n  - I also have concerns regarding the overall completeness of the experimental section; please see **Questions 1 and 2** for specific points.\n\n- **Methodological Design and Motivation**  \n  - The proposed method incorporates a pre-trained vision-language model (CLIP) as a complementary module within the SFDA framework while continuing to train the vision encoder of CLIP. This design choice introduces several questions: Why not use CLIP directly as the final target model? Could the CLIP vision encoder be better integrated into the target model? These concerns are further elaborated in **Question 1**.\n\n- **Presentation and Clarity**  \n  - The overall presentation and mathematical formulations could be improved for better clarity and readability. Please refer to **Question 4** for specific suggestions.\n\n\n**Reference**  \n[1] Proxy Denoising for Source-Free Domain Adaptation. Song Tang, et al. *ICLR 2025*.", "was_revised": true, "final_justification": "During the rebuttal, the authors addressed most of my concerns and explicitly discussed the role of fine-tuning the external pre-trained vision-language model in SFDA. I believe these technical discussions and evaluations could benefit the community, so I decided to update my score.", "rebuttal": "**Discussion on the use of CLIP**\n1. *How the improvements in CLIP representations contribute to the final adaptation performance*\n\nIn the proposed framework, fine-tuned CLIP contributes to performance improvement in two main aspects: **pseudo label refinement and knowledge distillation**.\n\nWe begin by analyzing ① **how the frozen CLIP model contributes to target model adaptation performance**, and then ② **examine the additional benefits brought by fine-tuning CLIP representations** within our framework.\n\nFor the ① analysis, we first compare two settings: (1) training the target model using our proposed framework without the frozen CLIP, and (2) training the target model with the frozen CLIP. This comparison quantifies how much the frozen CLIP improves the performance of the target model in our method.\n\n**Table G.** Performance comparison between target models trained with and without frozen CLIP on the Office-Home dataset.\n\n|Settings|→A|→C|→P|→R|Avg.|\n|-|-|-|-|-|-|\n|**Target w/o CLIP**|71.5|57.9|83.0|83.2|73.9|\n|**Target w/ CLIP**|**80.7**|**66.2**|**88.1**|**89.1**|**81.0**|\n\nHowever, as noted in Lines 87–89 of the main paper, the frozen CLIP model yields suboptimal representations for the target domain, underscoring the need for domain-specific adaptation. To address this (related to analysis ②), we fine-tune CLIP using TMI-based training.\n\nThe improved CLIP features significantly enhance target model performance through **both pseudo label refinement and knowledge distillation**, as shown in **Table H**.\n\n**Table H.** Performance comparison between target models trained using the frozen CLIP and fine-tuned CLIP (ours) on the Office-Home dataset.\n\n|Settings|→A|→C|→P|→R|Avg.|\n|-|-|-|-|-|-|\n|**Target w/o CLIP-tuning**|80.7|66.2|88.1|89.1|81.0|\n|**Target w/ CLIP-tuning**|**83.3**|**73.8**|**90.8**|**91.0**|**84.7**|\n---\n2. *Why not use CLIP directly as the final target model? / Is there any performance comparison between the fine-tuned CLIP encoder and the final adapted target model on the target domain?*\n\nThe primary objective of the proposed framework is to train the target model, not to directly use CLIP as the final model. The purpose of CLIP fine-tuning is therefore not to replace the target model, but rather to help the target model training with pseudo label refinement and knowledge distillation.\n\nAs shown in **Table I**, the final target model (Proposed) outperforms both the frozen (CLIP w/o tuning) and fine-tuned CLIP models (CLIP w/ tuning), confirming that CLIP is best used as an auxiliary module rather than the main predictor.\n\n**Table I.** Performance comparison on the Office-Home dataset among the frozen CLIP, fine-tuned CLIP via the proposed method, and the final target model trained through our proposed framework.\n\n|Settings|A→C|A→P|A→R|C→A|C→P|C→R|P→A|P→C|P→R|R→A|R→C|R→P|Avg.|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|**CLIP w/o tuning**|60.1|84.2|85.8|75.2|84.2|85.8|75.2|60.1|85.8|75.2|60.1|84.2| 76.3|\n|**CLIP w/ tuning**|72.2|89.8|90.1|82.2|89.9|90.1|81.6|72.4|90.3|82.2|72.4|90.0|83.6|\n|**Proposed**|**73.6**|**90.4**|**91.0**|**83.6**|**90.7**|**90.9**|**82.7**|**73.7**|**91.2**|**83.6**|**74.0**|**91.2**|**84.7**|\n---\n3. *Could the CLIP vision encoder be better integrated into the target model?*\n\nIn our framework, the CLIP encoder aligns visual features with text embeddings, while the target model aligns with classifier weights—reflecting fundamentally different objectives. This makes direct integration challenging, though exploring unified alignment strategies is a promising future direction.\n\n---\n**Further analysis on Pseudo label**\n1. *What is the quality of the filtered hard pseudo labels?*\n\nWe conducted a quantitative analysis of pseudo label (PL) accuracy and sampling ratio per cycle on the Office-Home dataset [32]. % values indicate PL accuracy, and values in parentheses show the sampling ratio.\n\nWhile full results were obtained, we report four representative tasks due to space limits. PL accuracy declined across cycles (92.7%, 89.8%, 88.4%, 87.5%) as more PLs introduced inevitable noise. However, prior work [a], [b] shows that such noise can improve generalization. Similarly, our model (**Table E**, discussed in our response to **NoPH’s Question 2**) benefits from using more samples—even noisy ones—rather than strictly controlling PL accuracy.\n\n**Table J.** Cycle-wise PL accracy and sampling ratio across different tasks using the proposed method.\n\n|Task|Cycle 1|Cycle 2|Cycle 3|Cycle 4|\n|-|-|-|-|-|\n|→A|93.91%(0.51)|90.68%(0.78)|88.82%(0.87)|87.49%(0.91)|\n|→C|86.50%(0.40)|81.33%(0.72)|79.13%(0.84)|77.87%(0.90)|\n|→P|94.46%(0.67)|93.00%(0.90)|92.51%(0.89)|92.13%(0.90)|\n|→R|95.77%(0.67)|94.08%(0.88)|93.30%(0.94)|92.66%(0.96)|\n\n---\n2. *Would incorporating the mixed soft prediction $\\mathcal{\\hat{p}}$ into the PLMatch framework further improve adaptation performance?*\n\nWe conducted additional experiments on the PLMatch framework using the mixed soft prediction $\\mathcal{\\hat{p}}$, applying both KL divergence and cross-entropy loss sequentially.\n\n**Table K.** Comparison of target model performance according to different label types and loss functions used in the PLMatch framework on the Office-Home dataset.\n\n|Label|Loss|→A|→C|→P|→R|Avg.|\n|-|-|-|-|-|-|-|\n|Soft |KL|82.7|72.5|90.6|90.6|84.1|\n|Soft |CE|83.0|73.4|90.6|90.8|84.4|\n|Hard|CE|**83.3**|**73.8**|**90.8**|**91.0**|**84.7**|\n\nWhile prior work [c] suggests that soft labels can be beneficial under label noise, our results show the opposite. We find that with high noise, soft labels may overly smooth targets and hinder convergence. Based on this interpretation and prior findings [d], we adopt a cross-entropy loss with hard pseudo labels in the PLMatch framework.\n\n---\n**Computational limitations**\n\nTo provide a clearer analysis of computational limitations, we report training time per batch (TTB), inference time (IT) per sample and GPU memory usage for the Art→Clipart task from the Office-Home dataset [32].\n\n**Table L.** Comparison of Computational Cost among SFDA Methods\n\n| Method | TTB ↓ (s) | IT ↓ (ms) | GPU memory ↓ (GB) |\n|-|-|-|-|\n|SHOT|0.3621|0.608|7.759|\n|DIFO|0.6576|0.608|8.340|\n|Ours| 0.5238 |0.608|14.452|\n\nAlthough our method requires higher GPU memory due to using both CLIP and the target model, its hierarchical structure—with separate cycle- and iteration-level processes—enables faster training than DIFO, which lacks such decomposition. Inference time per sample is identical across methods, as it depends only on the target model.\n\n---\n**SOTA comparison**\n\nThank you for your valuable observation. We have carefully examined the recent method (ProDe) you have referenced. Despite the emergence of new methods, our method still achieves state-of-the-art (SOTA) performance on two out of the three benchmark datasets (Office-Home [32] and VisDA-C [20]).\n\n**Table M.** Comparison of target model performance between recent method (ProDe) and ours on Office-Home dataset. The asterisk (*) indicates the performance reported in the referenced paper.\n\n|Method|A→C|A→P|A→R|C→A|C→P|C→R|P→A|P→C|P→R|R→A|R→C|R→P|Avg.|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|ProDe*|72.7|**92.3**|90.5|82.5|**91.5**|90.7|82.5|72.5|90.8|83.0|72.6|**92.2**|84.5|\n|Ours|**73.6**|90.4|**91.0**|**83.4**|90.7|**90.9**|**82.7**|**73.7**|**91.2**|**83.6**|**74.0**|91.2|**84.7**|\n\n**Table N.** Comparison of target model performance between recent method (ProDe) and ours on VisDA-C dataset.\n|Method|plane|bike|bus|car|horse|knife|mcycle|person|plant|sktbrd|train|truck|Avg.|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|ProDe*|98.3|**92.4**|86.6|**80.5**|98.1|**98.0**|92.3|84.3|94.7|97.0|94.1|**75.6**|91.0|\n|Ours|**98.6**|91.2|**89.8**|79.8|**98.1**|97.8|**95.0**|**84.7**|**96.2**|**97.2**|**95.0**|73.1|**91.4**|\n\nFor the remaining dataset, DomainNet-126 [19], we faithfully followed the official implementation provided by the referenced paper, including the pre-trained source model weights, codebase, and experimental settings. To account for potential performance variability, we conducted seven rounds of experiments, carefully varying the number of training epochs, random seeds, and other configurations. The asterisk (*) indicates the performance reported in the referenced paper.\n\n**Table O.** Comparison of target model performance between recent method (ProDe) and ours on the DomainNet-126 dataset. \n| Method|C→P|C→R|C→S|P→C|P→R|P→S|R→C|R→P|R→S|S→C|S→P |S→R |Avg.|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n| ProDe(10epochs)|76.47 ± 0.61|87.50 ± 0.53|73.17 ± 1.04|81.73 ± 0.70|87.97 ± 0.15|73.00 ± 0.10|81.10 ± 0.30|76.60 ± 0.26|73.97 ± 0.45|79.13 ± 0.55|76.13 ± 0.84|88.10 ± 0.36|79.57 ± 0.25|\n| ProDe(15epochs)|76.1|88.4|72.4|81.1|88.1|74.0|80.5|75.2|73.1|79.6|75.6|87.4|79.3|\n| ProDe(30epochs)|75.1|87.7|72.9|80.7|87.2|71.7|79.8|74.3|73.2|80.0|75.4|87.7|78.8|\n| ProDe*|83.2|92.4|79.0|85.0|92.3|79.3|85.5|83.1|79.1|85.5|83.4|91.0|84.9|\n| Ours|79.9|89.5|79.0|82.8|89.7|79.3|83.0|80.7|78.9|83.5|80.3|89.5|83.0|\n\nSpecifically, while the referenced method uses 15 epochs as its default training setting, firstly we also tested at its default training setting, without any changes. Subsequently, we tested alternative setups with fewer epochs (10) and more epochs (30). After identifying that the lower-epoch setting generally yielded better average performance, We further repeated the experiments with different random seeds. Despite these extensive efforts, we were ultimately unable to reproduce the performance reported in the original paper.\n\n---\n\n**References**\n\n[a] FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling. Bowen Zhang et al. NeurIPS 2021.\n\n[b] Boosting Semi-Supervised Learning by Bridging High and Low-Confidence Predictions. Khanh-Binh Nguyen et al. ICCV 2023.\n\n[c] Does Label Smoothing Mitigate Label Noise?. Michal Lukasik et al. ICML 2020.\n\n[d] To Smooth or Not? When Label Smoothing Meets Noisy Labels. Jiaheng Wei et al. ICML 2022."}, {"review_id": "nRhFyeCG7G", "reviewer": "Reviewer_9Bc5", "summary": "This work proposes a new method for source-free domain adaptation, consisting of several components as follows. To improve the quality of pseudo-labels, the proposed dual-facet pseudo-labeling strategy assigns labels only to target samples on which both the target model and CLIP agree. The vision encoder of CLIP is trained with a novel loss based on Tsallis Mutual Information (TMI), which has desirable theoretical properties. Leveraging TMI enables the method to gradually shift from exploration to exploitation. Finally, pseudo-labeled target samples are used to supervise the training of the target model; unlabeled samples are utilized for representation learning via consistency training; and a KL-divergence-based loss is applied to distill knowledge from CLIP to the target model. Experimental results demonstrate that the proposed method achieves competitive performance.", "questions": "1. I suggest that the authors provide a more detailed introduction to the principles and essential elements of Tsallis Entropy (TE) and Tsallis Mutual Information (TMI), as these concepts are not widely familiar to most readers. Additionally, the motivation for incorporating TE and TMI into the source-free domain adaptation (SFDA) framework should be more clearly justified. After all, this is the core contribution of the work.\n\n2. The authors are encouraged to improve the clarity and readability of the manuscript. Careful language polishing will help ensure the proposed ideas are effectively conveyed.\n\n3. A specific question: What exactly do the terms “cycle-level process” and “iteration-level process” mean in the context of this paper? This phrasing is somewhat unclear and may require clarification or rewording for better comprehension.", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\nThe main contribution of this work is the introduction of Tsallis Entropy (TE) and Tsallis Mutual Information (TMI) into the area of source-free domain adaptation, which provides a novel and potentially valuable perspective.\n\nWeaknesses:\n1. Apart from TE and TMI, other components of the proposed method are relatively conventional. Dividing target samples into easy and hard ones is intuitive and has been explored in prior works [1, 2]. Utilizing unlabeled samples through consistency training is a standard technique in self-supervised learning, particularly in contrastive learning frameworks [3]. Knowledge distillation is also widely adopted when leveraging pre-trained models such as CLIP [4]. Therefore, the novelty of these components—except the TMI-based loss—is limited. Moreover, the introduction and motivation for incorporating TE and TMI are not sufficiently detailed or justified.\n2. The writing quality of the manuscript is subpar, which significantly affects readability and comprehension. There are multiple grammatical errors, even in the Abstract, which should be carefully avoided. For instance, the abstract sentence: “In a self-supervised manner, relying on pseudo labels on target domain samples facilitates the domain adaptation performance providing strong supervision.”\nis grammatically incorrect and semantically ambiguous. I suggest rephrasing it for clarity and conciseness. One possible revision could be: “In a self-supervised setting, pseudo labels on target domain samples provide strong supervision, thereby improving domain adaptation performance.” Overall, the language quality is not adequate for publication and requires substantial improvement to enhance clarity and readability.\n\nReferences:\n\n[1] Yang, Jianfei, Xiangyu Peng, Kai Wang, Zheng Zhu, Jiashi Feng, Lihua Xie, and Yang You. \"Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors.\" *The Eleventh International Conference on Learning Representations.*\n\n[2] M. Xia et al., “A Separation and Alignment Framework for Black-Box Domain Adaptation,” *Proceedings of the AAAI Conference on Artificial Intelligence*, Mar. 2024, pp. 16005–16013. doi: 10.1609/aaai.v38i14.29532.\n\n[3] K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum Contrast for Unsupervised Visual Representation Learning,” CVPR 2020\n\n[4] Liang, Jian, Dapeng Hu, Jiashi Feng, and Ran He. \"Dine: Domain adaptation from single and multiple black-box predictors.\" In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 8003-8013. 2022.", "was_revised": true, "final_justification": "I have no further questions, and I am raising the score to 5. I hope the authors will revise their paper accordingly.", "rebuttal": "**(W1, Q1) Limitied novelty beyond TE and TMI**\n\nThank you for your thoughtful feedback. We sincerely appreciate the reviewer’s careful attention to whether the contributions of our work are sufficiently distinct and clearly communicated.\n\n* **Tsallis entropy** \n\nThe contribution of our study lies in associating Tsallis entropy—which can either emphasize or suppress prediction uncertainty—with the model's degree of adaptation to the target domain. This enables the model to adopt different strategies for sample exploration and exploitation depending on its own adaptation stage. This strategy has not been previously explored in the SFDA field. \n\n* **Dual-facet pseudo labeling strategy** \n\nMoreover, the proposed dual-facet pseudo labeling strategy offers a simple yet practical approach. By using the intersection of predictions from CLIP (which provides a domain-invariant perspective) and the target model (which offers a task-specific perspective) as pseudo labels, our method delivers strong pseudo supervision without relying on complex pseudo labeling mechanisms. As shown in lines 273–277 of the main paper, using only these pseudo labels still achieves performance that surpasses most existing SFDA methods. We would greatly appreciate it if this aspect could also be considered as a meaningful contribution.\n\nNow, we will strengthen the expressions in the Introduction and Preliminary sections based on the contributions related to Tsallis entropy and Tsallis Mutual Information.\nSpecifically, as shown in Appendix B, TE allows us to assign varying importance  to different levels of prediction confidence by adjusting the entropy index $\\mathcal{q}$. \n\n> When $\\mathcal{q > 1}$, the loss function emphasizes low-confidence predictions, encouraging broader exploration. \n\n> When $\\mathcal{q < 1}$, the loss function focuses on high-confidence samples, enabling focused exploitation.\n\nOur method leverages this property by incorporating it into the mutual information objective, enabling a dynamic exploration–exploitation strategy during CLIP vision optimization.\nIn the early training stages, it suppresses overconfident predictions and encourages more cautious learning. \nIn later stages, it shifts toward reinforcing reliable samples. \nThis exploration–exploitation trade-off, modulated by model uncertainty, is particularly effective in scenarios with large domain gaps, where overfitting to early predictions can harm generalization.\nTo better highlight this unique contribution, we will revise the Introduction to more clearly articulate the role and motivation for using TE and the adaptation index in our learning framework. \n\n---\n**(W2, Q2) Clarity and readability of the manuscript**\n\nWe sincerely apologize for not ensuring sufficient clarity and readability in the main paper. We will thoroughly review the main paper and make sure to revise any unclear or low-quality expressions. \n\n- **Abstract**\n\n1. As pointed out in Weakness 2, we will revise lines 2–4 to “In a self-supervised setting, pseudo labels on target domain samples provide strong supervision, thereby improving domain adaptation performance.”\n2. In lines 4–6, we will change “However, the critical problem” to “However, a critical problem”.\n3. In lines 9–10, we will revise “target samples with agreement between a target model and CLIP” to “target samples on which the target model's predictions and CLIP's predictions agree”.\n\n- **Introduction**\n\n1. In line 44, we will change “paradigm in the SFDA” to “paradigm in SFDA”.\n2. In line 51, we will revise “using a combination of supervised loss” to “using a combination of a supervised loss ”.\n\n- **Preliminary**\n\n1. In line 127, we will update the phrase “that enforces the model to produce consistent predictions” to “that ensures the model produces consistent predictions”.\n\n- **Methodology**\n\n1. In line 234, we will revise “leverages unlabeled samples using consistency training,” to “leverages unlabeled target samples through consistency training”.\n\n- **Experiments**\n\n1. In lines 273–274, we will revise “First, when using only the pseudo supervision loss with hard pseudo labels,” to “First, when only the pseudo supervision loss with hard pseudo labels is used.”\n\n\n\n---\n**(Q3) Clarification of \"cycle-level process\" and \"iteration-level process\"**\n\nWe apologize for any confusion caused by the complexity of our framework design. To improve clarity, we will revise the Overview section of the manuscript to more clearly explain the two-level structure.\nFor clearer understanding, we provide a summary table of the operations and learnable modules at each process stage, along with the overall pseudocode of the proposed framework.\n\n|Process|Key Operations|Learnable Module|\n|-|-|-|\n|**Cycle-level**|Generate pseudo labels from CLIP and target model predictions (CPG)| (None) |\n| |Fine-tune CLIP encoder using Tsallis Mutual Information objective (DVO)|CLIP vision encoder|\n| **Iteration-level**|Train target model with fixed pseudo labels from current cycle (PLMatch)|Target model|\n\n1. Cycle-level process\n* Refers to the outer training loop in our framework.\n* In each cycle:\n> * Pseudo labels are generated based on the predictions of CLIP and the target model. (Calibrated pseudo label generation, CPG)\n> * The CLIP vision encoder is fine-tuned using the proposed Tsallis Mutual Information (TMI) objective to adapt to the target domain. (Dynamic entropy-guided vision optimization, DVO)\n\n2. Iteration-level process\n* Refers to the inner training loop within each cycle.\n* In each iteration:\n> * The target model is trained using the pseudo labels fixed for the current cycle. (Pseudo label matching framework, PLMatch)\n* Unlike the cycle-level process, the iteration-level process does not update pseudo labels or CLIP but focuses soley on optimizing the target model.\n\n```python\n# Pseudo-code for proposed framework\n\nfor cycle in range(num_cycles):  # Cycle-level process\n    # Step 1: Calibrated Pseudo Label Generation (CPG)\n    soft_pseudo_labels, hard_pseudo_labels = generate_calibrated_pseudo_labels(CLIP, target_model, target_data)\n\n    # Step 2: Dynamic Entropy-guided Vision Optimization (DVO)\n    CLIP = optimize_CLIP_with_TMI(CLIP, soft_pseudo_labels, target_data)\n\n    for iteration in range(num_iterations):  # Iteration-level process\n        # Step 3: Pseudo Label Matching (PLMatch)\n        target_model = train_with_PLMatch(target_model, hard_pseudo_labels, target_data, CLIP)\n\n        # No updates to CLIP or pseudo labels here — only optimize the target model\nreturn target_model\n```"}, {"review_id": "0WrbOZ9dm4", "reviewer": "Reviewer_EPpg", "summary": "The paper tackles source-free domain adaptation (SFDA) by jointly (i) calibrating pseudo labels from a task-specific classifier and CLIP (“dual-facet” hard labels) and (ii) fine-tuning the CLIP vision encoder with a Tsallis-mutual-information (TMI) loss modulated by an uncertainty-based adaptation index ψ. The combination is trained in repeated cycles of Calibrated Pseudo-label Generation (CPG), Dynamic Vision Optimization (DVO) and Pseudo-Label Matching (PLMatch) (Fig. 3). DUET achieves state-of-the-art accuracy on Office-Home, VisDA-C and DomainNet-126, improving the best prior method DIFO by 1.4 – 2.9 pp on average.", "questions": "See Weaknesses.", "limitations": "NA", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. Well-motivated dual supervision: Requiring agreement between CLIP and the task model before assigning a hard label reduces noise and yields better-calibrated confidence.\n2. Novel uncertainty-aware TMI objective: The ψ-controlled entropy index smoothly anneals from exploration (ψ > 1) to exploitation (ψ < 1), stabilising training and showing clearer loss dynamics than standard MI.\n3. Thorough empirical study: Main results on three benchmarks, plus ablations on loss components/weights (Tab. 3) and analyses of calibration, t-SNE, and confusion matrices.\n\nWeaknesses:\n1. Limited statistical rigour – Results are single-run averages; no variance or significance tests are reported.\n2. Accuracy improves but requires up to 8 cycles × 4 iterations of target-model updates and several vision-encoder updates.\n3. CLIP dependence – Method assumes access to a large ViL foundation model.", "was_revised": true, "final_justification": "After reviewing it, I have no further questions and would prefer to retain my current rating.", "rebuttal": "**(W1) Limited statistical rigour**\n\nWe appreciate the reviewer’s comment on the importance of statistical rigour in evaluating experimental results.\n\nTo address this concern, we have conducted additional experiments using three different random seeds (2020, 2023, and 2025) and reported both the mean and standard deviation for each adaptation task across all three datasets (Office-Home [32], VisDA-C [20], and DomainNet-126 [19], respectively). These results provide a more comprehensive and reliable evaluation of our method.\n\nTo maintain clarity in the main body, we will include the full statistical results in the Appendix section of the revised manuscript.\n\n**Table Q.** Experimental results and statistical rigor on the Office-Home dataset under varying seeds.\n\n|Seed|A→C|A→P|A→R|C→A|C→P|C→R|P→A|P→C|P→R|R→A|R→C|R→P|Avg.|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n| 2020 |73.6|90.4|91.0|83.6|90.7|90.9|82.7|73.7|91.2|83.6|74.0|91.2|84.7|\n| 2023 |73.4|90.4|90.9|83.4|90.8|90.8|83.0|73.6|91.0|83.8|74.2|91.2|84.7|\n|2025|73.0|90.4|90.8|83.3|90.8|90.8|83.1|73.3|90.9|83.9|73.8|91.3|84.6|\n|**–**|**73.27 ± 0.3**|**90.43 ± 0.1**|**90.87 ± 0.1**|**83.43 ± 0.2**|**90.73 ± 0.1**|**90.83 ± 0.1**|**83.00 ± 0.3**|**73.33 ± 0.4**|**90.93 ± 0.3**|**83.73 ± 0.2**|**73.77 ± 0.3**|**91.20 ± 0.1**|**84.63 ± 0.1**|\n\n**Table R.** Experimental results and statistical rigor on the VisDA-C dataset under varying seeds.\n\n|Seed|plane|bike|bus|car|horse|knife|mcycle|person|plant|sktbrd|train|truck|Avg.|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|2020|98.7|91.1|89.8|79.1|98.2|97.8|95.1|84.2|96.0|97.3|95.4|73.9|91.4|\n|2023|98.6|91.4|88.5|78.6|98.3|98.1|95.2|84.9|96.0|97.1|95.4|73.6|91.3|\n|2025|98.7|89.7|88.2|79.1|97.9|97.5|96.2|85.8|96.5|97.3|95.0|72.8|91.2|\n|**–**|**98.7 ± 0.0**|**90.7 ± 0.7**|**88.8 ± 0.7**|**78.9 ± 0.2**|**98.1 ± 0.2**|**97.8 ± 0.2**|**95.5 ± 0.5**|**85.0 ± 0.7**|**96.2 ± 0.2**|**97.2 ± 0.1**|**95.3 ± 0.2**|**73.4 ± 0.5**|**91.3 ± 0.08**|\n\n**Table S.** Experimental results and statistical rigor on the DomainNet-126 dataset under varying seeds.\n\n|Seed|C→P|C→R|C→S|P→C|P→R|P→S|R→C|R→P|R→S|S→C|S→P|S→R|Avg.|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|2020|79.9|89.5|79.0|82.8|89.7|79.3|83.0|80.7|78.9|83.5|80.3|89.5|83.0|\n|2023|80.3|89.6|79.1|82.6|89.7|78.7|82.8|80.7|79.0|83.0|80.5|89.6|83.0|\n|2025|80.1|89.6|78.9|83.1|89.8|78.9|82.9|80.7|79.0|83.1|80.5|89.6|83.0|\n|**-**|**80.1 ± 0.2**|**89.6 ± 0.1**|**79.0 ± 0.1**|**82.8 ± 0.3**|**89.7 ± 0.1**|**79.0 ± 0.3**|**82.9 ± 0.1**|**80.7 ± 0.0**|**79.0 ± 0.1**|**83.2 ± 0.3**|**80.4 ± 0.1**|**89.6 ± 0.1**|**83.0 ± 0.1**|\n\n---\n**(W2) Computational Overhead**\n\nWe appreciate the reviewer’s observation regarding the increased number of updates during training.\nWe do acknowledge that there is a trade-off between accuracy and the cost of updating the CLIP model parameters.\nTo further investigate this, we conducted additional experiments on the VisDA-C dataset [20], varying the number of cycles and iterations while keeping the total number of target model updates fixed (the number of cycles × the number of iterations). The results indicate that increasing the number of CLIP vision encoder updates (conducted by cycle-level process) leads to consistent improvements in accuracy, even with the same number of target model updates.\nBased on these findings, although the proposed method inherently requires more computational resources due to the involvement of both CLIP and the target model, we would like to emphasize that it provides a flexible structure that allows users to adjust the trade-off between computational cost and performance. Moreover, our final setup carefully balances accuracy and training efficiency across all experiments.\n\n\n**Table T.** Comparison of target model performance with varying the number of CLIP updates (cycles) under a fixed number of target model updates (the number of cycles × the number of iterations). The asterisk (*) denotes the cycle-iteration combination employed in the final performance.\n|Cycle|Iteration|plane|bike|bus|car|horse|knife|mcycle|person|plant|sktbrd|train|truck|Avg.|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|4|8|98.6|88.5|**91.5**|78.2|98.0|97.4|94.6|**85.5**|95.6|97.0|94.4|69.8|90.8|\n|8*|4*|98.6|91.2|89.8|**79.8**|**98.1**|97.8|**95.0**|84.7|96.2|**97.2**|**95.0**|73.1|91.4|\n|16|2|**98.7**|**92.0**|90.0|79.0|**98.1**|**98.1**|**95.0**|85.2|**96.8**|97.0|94.8|**73.3**|**91.5**|\n\n|Cycle|Iteration|Training time (s)|\n|-|-|-|\n|4|8|17426|\n|8*|4*|18641|\n|16|2|21933|\n---\n**(W3) CLIP dependency**\n\nWe acknowledge the concern regarding CLIP dependence. However, we respectfully ask the reviewer to consider that our proposed method does more than simply utilize CLIP—it is fundamentally designed to provide deeper insights into SFDA through a carefully crafted training strategy and framework architecture.\n\nFirst, the proposed Tsallis Mutual Information (TMI) offers an important insight that SFDA training should adopt an exploratory strategy in the early stages and shift toward exploitation in the later stages. Moreover, TMI allows this transition to occur automatically based on the model’s adaptation status to the target domain.\n\nSecond, our hierarchical training structure—comprising the cycle-level and iteration-level processes—and the dual-facet pseudo labeling strategy enable robust performance even without CLIP, as demonstrated in our ablation study.\n\n\n**Table U.** Comparison of target model performance with SHOT and our proposed framework without CLIP on the Office-Home dataset.\n\n|Settings|Venue|A→C|A→P|A→R|C→A|C→P|C→R|P→A|P→C|P→R|R→A|R→C|R→P|Avg.|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|**SHOT** [11]|PMLR 2020|55.0|78.8|81.3|69.1|79.1|79.0|68.0|54.8|81.8|73.6|58.9|83.5|71.9|\n|**AaD** [a]|NIPS 2022|59.3|79.3|82.1|68.9|79.8|79.5|67.2|57.4|83.1|72.1|58.5|85.4|72.7|\n|**C-SFDA** [b]|CVPR 2023|60.3|80.2|82.9|69.3|80.1|78.8|67.3|58.1|83.4|73.6|**61.3**|86.3|73.5|\n|**I-SFDA** [c]|CVPR 2024|**60.7**|78.9|82.0|69.9|79.5|79.7|67.1|**58.8**|82.3|74.2|**61.3**|**86.4**|73.4|\n|**SHOT+DPC** [d]|CVPR 2024|59.2|79.8|82.6|68.9|79.7|79.5|68.6|56.5|82.9|73.9|61.2|85.4|73.2|\n|**Target w/o CLIP**|-|58.1|**81.9**|**83.6**|**70.7**|**81.1**|**80.8**|**69.5**|55.7|**85.1**|**74.3**|59.9|86.0|**73.9**|\n\nSpecifically, to assess the effectiveness of our framework independently of the CLIP model, we compared the performance of the target model across various SFDA methods that don't use CLIP. For fair comparison, we replaced CLIP-based pseudo labels with those generated by SHOT [11] in our SFDA method. This setting corresponds to Target w/o CLIP in **Table U**. Notably, both SHOT [11] and Target w/o CLIP use the same pseudo labels (from SHOT), yet our framework achieves a +2.0% accuracy improvement over SHOT [11]. This demonstrates that our hierarchical learning design and dual-facet strategy are effective for SFDA. Furthermore, our method outperforms all other methods as well as SHOT+DPC [d], a recent SHOT-based approach. This highlights that our framework is highly effective for SFDA and serves as a competitive baseline. \nIn summary, while CLIP is effective in our framework, the strong results of our SFDA framework achieved even with pseudo labels generated by SHOT (not CLIP) show that the core strength of our method lies in the framework itself.\n\n---\n**References**\n\n[a] Attracting and Dispersing: A Simple Approach for Source-Free Domain Adaptation. Shiqi Yang et al. NeurIPS 2022.\n\n[b] C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source-Free Domain Adaptation. Nazmul Karim et al. CVPR 2023.\n\n[c] Understanding and Improving Source-Free Domain Adaptation from a Theoretical Perspective. Yu Mitsuzumi et al. CVPR 2024.\n\n[d] Discriminative Pattern Calibration Mechanism for Source-Free Domain Adaptation. Haifeng Xia et al. CVPR 2024."}]

Author rebuttal:

Meta-review: Dear authors, 

Thank you for submitting draft. This draft has received overall positive reviews, including 1 Accept (Rating-5). We strongly suggest that authors should incorporate suggestions and elements presented during discussion and rebuttal in the final draft. 

Congratulations.

regards
AC
Decision: Accept (poster)