Exposing Mixture and Annotating Confusion for Active Universal Test-Time Adaptation
Authors: Jiayao Tan
Fan Lyu
Chenggong Ni
Fuyuan Hu
Wei Feng
Rui Yao
Venue: ICLR 2026 Poster (2026)
Keywords: Test-Time Adaptation
Open-set
Primary area: transfer learning, meta learning, and lifelong learning

Abstract: Universal Test-Time Adaptation (UTTA) tackles the challenge of handling both class and domain shifts in unsupervised settings with stream testing data. Currently, most UTTA methods can only deal with minor shifts and heavily rely on heuristic approaches. To advance UTTA under dual shifts, we propose a novel Active Universal Test-Time Adaptation (AUTTA) framework, Exposing Mixture and Annotating Confusion (EMAC), which incorporates active human annotation into the UTTA setting. To select appropriate samples for annotation in AUTTA, we first identify the mixed regions of target domain samples under dual shifts, highlighting potential candidate samples. We then design a reward-guided active selection strategy to prioritize annotating the most representative samples within this set, maximizing annotation effectiveness. Additionally, to balance the use of pseudo-labels with the limited number of annotations, we propose an adaptation objective designed to address the adaptation imbalance caused by annotation scarcity. Extensive experiments show that the proposed AUTTA approach significantly improves performance and achieves state-of-the-art.

Published as a conference paper at ICLR 2026
EXPOSING MIXTURE AND ANNOTATING CONFUSION FOR ACTIVE UNIVERSAL TEST-TIME ADAPTATION
Jiayao Tan 12, Fan Lyu"; Chenggong NI,, Fuyuan Hu²t Wei Feng", Rui Yao"
School of Artificial Intelligence, Tianjin University
Suzhou University of Science and Technology
"Computer Vision Centre, Universitat Autônoma Barcelona
"School of Computer Science, Tianjin University
$China Unviersity of Mining and Technology
j jiayaotanet eueee..uuuddd... edu. en, fanlyuêcve. wab. wfenggieee org {cgn&post, Fuyuanhusmail) usts edu, raiyao@cunt, edu. cn
ABSTRACT
Universal Test- Time Adaptation (UTTA) tackles the challenge of handling both class and domain shifts in unsupervised settings with streaming test data. How- ever, existing UTTA methods are often limited to minor shifts and heavily rely on heuristic rules. To advance UTTA under dual shifts, we propose a novel frame- work, Active Universal Teme-Time Adaptation (AUTTA), and instantiate it with Exposing Mixture and Annotating Confusion (EMAC), which incorporates active human annotation into the UTTA setting. To select appropriate samples for an- notation in AUTTA, we first identify the mixed regions of target-domain samples under dual shifts, thereby exposing reliable candidate samples. We then design a reward-guided active selection strategy to prioritize annotating the most representa- tive samples within this set, maximizing the effectiveness of limited annotations. In addition, to balance pseudo-labels with scarce annotations, we introduce an adaptation objective that mitigates the imbalance and alleviates decision-boundee boundary ambiguity. Extensive experiments demonstrate that AUTTA significantly improves performance and achieves sate-of-the-as aht results under dual-shift scenarios.
INTRODUCTION
Universal Test-Time Adaptation (UTTA) (Schlachter & Yang 2024 Schlachter et al. 2025 Li et al. 2023ab is a framework designed to effectively learn new classes while excluding old classes in the target domain during testing. It aims to tackle both domain shift and class shift without requiring access to source data, making it particularly suitable for real-world scenarios with evolving data distributions. However, when domain and class shifts occur simultaneously, pseudo-labeling methods tend to fail due to increased labeling errors. This paper introduces active learning framework for UTTA, termed Active Universal Test-Time Adaptation (AUTTA), by incorporating a small number of
samples during testing. practical example of AUTTA is auto-driving, where human intervention can be queried to provide small yet essential annotations of high- high-uncertainty scenarios, improving robustness, (See the AppendixA..1 for detail application settings.)
Nevertheless, AUTTA is not simply an enhancement of traditional active learning methods within the UTTA framework. This because traditional active learning (Settles) (209)) methods typically select samples for annotation based on entropy or uncertainty. However, previous study (You et al. 2019) shows that under dual transfer scenarios, these methods tend to select samples distributed with higher class shift, which not only reduces annotation effectiveness but also introduces significant biases in model training. To illustrate this, we conduct an in-depth analysis of the data distribution the target domain and validate it through experiments, shown in Fig. T The results show that samples in regions focused on class shift QQaa et al. 20233] Zhao et al., 20255; Majee et al., 2024) or domain shift exhibit lower pseudo-label error rates, while those in dual-shift mixed regions have higher error
"quual contribution
Corresponding authors: Wei Feng (first), Fuyuan Hu

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Published as a conference paper at ICLR 2026
Domain Shift
Shift
Domain!
Domain2
Domain3
Entropy
Random
FMAC
Sample distribution in target domain
Pseudo-labels error
(c) Number of annotations per method
In the sample distribution under both class and domain dual shifts (a), mixed region B exhibits a higher degree of sample distribution confusion and a higher pscudo-label error rate (b). This results in more significant annotation effects. Traditional active learning methods fail to effectively seleet samples from region for annotation (c), Icading to wasted annotation costs. Best viewed in dark bluc.
Figure 1:
rates. Therefore, annotations should prioritize mixed regions ensure their effectiveness. However, existing methods struggle to effectively cover these critical regions. Inspired by this, We propose to expose samples in the mixed region under dual shifts and achieve effective annotation in AUTTA.
In this paper, we introduce a novel method called Exposing Mixture and Annotating Confusion (EMAC) to select appropriate samples for annotation in AUTTA improve the adaptation at test time. The sample annotation process in EMAC involves two stages. Exposing the mixture performs initial filtering to generate candidate samples with dual shifts. Annotating confusion refines the selection from the candidates. Specifically, we first design to expose mixed sample candidates using a Gaussian Mixture Model (GMM) to model the features after orthogonal decoupling, which prevents invalid labeling of other regions. Second, we introduce which can be theoretically connected to information gain in active learning (Gal et al.) 2017 Sener & Savarese 2018). Rather than relying purely on heuristic uncertainty measures, it quantifies the marginal reduction of entropy for old classes and the increased separability for new classes, aligning with the goal of minimizing adaptation error under dual shifts. With limited annotation budgets, this strategy prioritizes the most informative samples from the mixed regions, thereby maximizing annotation effectiveness. Finally, design clustering blased contrastive optimization objective to overcome the fuzzy decision boundary problem caused by limited annotated samples, combining annotation information with the pseudo-labels information balanced from unsupervised data. This effectively prevents the model from overfitting (Liu et al., 2023 Lin et al., 2020) and ensures consistent performance improvement. EMAC achieves state-of of-te-of-the-are the-art art (SOTA) performance on both the DomainNet and VisDA-C datasets. Compared to existing UTTA methods, EMAC leverages human assistance to better learn inter-class distinctions in open-set scenarios. Additionally, unlike traditional active learning methods, EMAC excels in selecting the most representative and informative samples, effectively balancing annotation cost and model performance.
reward-guided selection strategy,
Our contributions are as follows:
(1) We propose novel Universal Test Time Adaptation paradigm, Active Universal Test-Time Adaptation (AUTTA), which enhances the applicability and generalization of UTTA in open- class shift scenarios by effectively integrating additional information.
(2) We propose new of selected samples in open-set scenarios. Additionally, by incorporating a clustering- contrastive objective, the approach improves robustness under the AUTTA protocol.
active learning approach that ensures the informativeness
(3) We establish benchmark for evaluating the AUTTA protocol, encompassing various types of domain shifts and class shift ratios, including common corruptions and style transfer. Our approach achieves state-of-the-ee performance on the proposed benchmark.
2 RELATED WORK
2.1 UNIVERSAL TEST- TIME ADAPTATION
Universal Test-Tine Time Adaptation (UTTA) Schlachter et al. 2025) designed address the challenge of domain and class shifts that are prevalent in open-world environments. Unlike traditional TTA (Sun et al. 2020), which assumes the test data to be somewhat aligned with the source domain,
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Published as a conference paper at ICLR 2026
Step Selecting from Candidates (Section 3.3)
Domain Shift
GMM
A
*
B
Mosial
Amnotation
Sample candidates
Representative samples
Class Shift
Step Exposing Mixed Sample Candidates B (Section 3.2)
Training Aduptation (Section 3.4)
Figure 2: Sample selection and annotation process.
UTTA accounts for more complex situations where test data may include not only domain shifts (yu et al., 2023 Ni et al. 2025 Lyu al.) 2021) but also new, unknown categories noise. This comprehensive approach ensures that the model can adapt in real time while handling diverse, uncertain and unpredictable test data. By focusing
such challenging scenarios, UTTA aims to improve the adaptability (Seabra et al. 2024 Qu et al. 2024a Liu et al. 2025) and robustness of models in open-world settings, making a more universal solution compared conventional TTA. Specifically, TTAC (Su et al. (022) employs distribution alignment at both global and category levels to facilitate test-time adaptation. OPTTT (Li al. 2023b) combines self- liff-trainigg with prototype expansion to accommodate the strong OOD samples. Despite the progress made by methods such as TTAC and OPTTT, traditional approaches often struggle address the need for sample diversity and the high uncertainty associated with real-world test-time scenarios, particularly without the incorporation of additional information.
2.2 ACTIVE LEARNING AND ATTA
Active Test-Time Adaptation (ATTA) (Gui et al. 2024) is a promising and lightweight domain adaptation method designed optimize model generalization (Li et al. (024) by integrating small number of labeled samples during test time. Unlike most TTA methods, which can only handle small changes and heavily rely on heuristics and empirical studies (Pearl 2009 Lin al. 2022). ATTA combines active learning strategies and utilizes unlabeled data collected at test time to improve model performance through real-time sample selection and adaptation. Without access to source domain data, ATTA dynamically adjusts the model by detecting changes in the target domain, achieving better adaptation. However, the current ATTA algorithm inherits the limitations of Active Domain Adaptation (ADA) (Sener & Savarese. 2018 Rangwani et al. [021]] Xie et al. (2022) and Active Online Learning (AOL) (Cacciarelli & Kulahci| 2023). These methods typically focus on sample selection strategies in the context of drifting data streams (Zhou al., 102 Baier et al. 1 Li al. 2021) under domain shifts, without accounting for class shifts. As a result, in scenarios involving class shifts, the ATTA approach, similar general active learning methods, often fails to address the need for both sample diversity and high uncertainty.
METHODOLOGY
3.1 PROBLEM DEFINITION
Active Universal Test-Time Adaptation (AUTTA) integrates active learning into the test-time adap- tation process address both class and domain shifts. In AUTTA. the model dynamically selects a small set of instances for annotation from an oracle (e.g., human annotators or self-supervised signals). The acquired labels are then incorporated into the adaptation process, providing additional supervision alongside pseudo-labeled data. Similar to UTTA, an AUTTA model is initialized from pre-trained model f(x; 0) trained on the source domain Ds, and is then deployed an unlabeled target test domain DUT (x.) for simultaneous testing and adaptation. At each step, the active learning algorithm queries the oracle for a limited number of labels, which are accumulated into a labeled target set Drr. Importantly, annotations may be partially acquired. reflecting the practical constraints of limited labeling budgets. The ultimate goal AUTTA is to leverage these annotations accurately classify old classes in the target domain while rejecting novel ones.
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Published as a conference paper at ICLR 2026
As illustrated in Fig. 2 approach proceeds in two stages. First, employ Gaussian Mixture Model (GMM) (Qu et al. 2024b; Wang & Wang 2024) to expose regions where samples are most likely to exhibit dual shifts, generating candidate pool that captures the most informative samples. This coarse selection ensures that annotation resources are not wasted on low-value samples. Second, evaluate the contribution of candidate samples the model's ability distinguish old from new classes, and select those with the greatest impact. This coarse-to-line pipeline improves the efficiency of annotation under strict budget constraints. Finally, address the class boundary ambiguity caused by scarce labeled data, we introduce clustering- -ased contrastive loss that jointly leverages both annotations and pseudo-labels, thereby enhancing boundary clarity and improving robustness during test-time adaptation.
3.2 EXPOSING MIXED SAMPLE CANDIDATES
To tackle the mixture, we need to first expose the highly mixed regions between the new and old class samples and present candidates. Due to the unavailability of source data, we leverage the orthogonality and low-rank approximation properties of Singular Value Decomposition (SVD) to decompose the classifier Wels RCx parameter space, which encapsulates source domain
knowledge, into independent subspaces:
Wels F Fknown F unknown'
F
Eq. equation enables the decoupling of source-known vector base Fknown and the source-unknown vector base F unknown. effectively facilitating the separation of known and unknown information under class and domain shifts when source data is inaccessible. Let z R Bx represent the normalized features of a batch of target data D
2 (DPb 0).
Then, is projected onto F Fknown and F Funknown as follows:
Zknown = 2 (1:()) F known:
(C++1D) FC+1.D
Zunknown
unknown:
where, M
Zunknown
estimate the distribution of unknown information in Lunknown 12. Our observations show that the empirical distribution of [| 112 exhibits a bimodal pattern, with two distinct peaks, typically indicating the presence of old classes and new classes of data. Therefore, we use bimodal Gaussian mixture model to estimate the distribution of (unknown 16 where the component with a lower mean represents old classes data, and the component with a higher mean represents new classes data. Specifically, we use pold and fnew to represent the expectations old classes and new classes data, with Pold Spnoww The Probability Density Function (PDF) is as follows:
Intuitively, through feature decomposition,
p Z(Zuknoow) =
x.N (Hold Poia) + (1 π N) 2ew)).
(4)
where, π 0.5. We define three types of sample feature regions using Hold and Unew as follows:
Xola Durr(e
1 (z Zunknown <Mota)],
(5)
Xnew = Dur
Σ
1 ( (Zunknown' > (new)) )}
(6)
Xmix Dur [i]
(Pold Lunknownn <(nnww)]
(7)
Using the GMM, three distinct regions are identified. Samples from the two peripheral regions (Nold and Xnew) are assumed to experience higher levels of domain or class shifts. However, selecting samples with them requires more annotations and fails to address the dual shift confusion issue effectively. Alternatively, pseudo-labeling can partially mitigate this issue but lacks robustness. In contrast, samples from the mixed region Xmix exhibit significant confusion due to the dual shift effect. Selecting samples from this region for annotation offers higher potential benefits, as these samples contribute more to resolving the mixture caused by overlapping shifts. Therefore, we select samples from the mixed region candidate samples to maximize annotation effectiveness, thereby enhancing the model's adaptability and performance under dual-shift conditions. Further discussion of GMM details in the appendix A.2
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3.3 SELECTING FROM CANDIDATES
Although the above method provides a set of sample candidates, distributional bias within these candidates result in severe annotation imbalances samples randomly selected. To address this issue, we propose a reward-guided active selection strategy, which prioritizes annotating representative samples that contribute most model optimization.
Formally, we design a Max-Min Entropy (MME) objective function:
LMME
Told + T new:
(
where oldd and I new quantify the marginal information gain of annotated samples for old and new classes, respectively. This design is theoretically connected active learning bounds: maximizing entropy reduction of old classes improves the retention of known decision boundaries, while maxi- mizing entropy increase for new classes enhances the margin between known and novel categories. Thus, LME can be interpreted as minimizing an empirical generalization error bound under limited annotation budgets.
Specifically, old measures how annotations improve the retention of old-class knowledge, i.e., the model's increased confidence in recognizing old classes:
old Σ H( 0')) - (Hooaoo 0'-)),
where H-)) denotes the entropy function. Conversely, I new measures the extent which annotations help exclude new classes by increasing the model's confidence in rejecting them:
I new
Σ H[f(Xnew 0²- HJ 0*)).
(10)
To further balance annotation efforts between old and new classes, we define average reward values for both types of annotated samples:
Rita
(Wold Pold + (1 Wnew) Tnew):
(11)
Di
Rnew
R
((1 - Wold) -oold + Wnew Inew).
(12)
IDEL
where Dild = (xxp yolld )) and Deaw = [(x:eew ((a.eee ) denote labeled samples from old
and new classes, respectively. Here, Wold and whew are weighting factors reflecting the contribution ratio of old and new classes. These weights can be interpreted as balancing terms ensuring that the variance of the empirical risk estimator does not dominate in either direction.
To stabilize selection, we further apply exponential moving average (EMA) update:
Rold Rold, + a) R)
(13)
Ruew = a Rnew,t + (1 a)) R)
(14)
where is the smoothing factor. This temporal smoothing aligns with theoretical analyses of regret minimization in online learning, ensuring that short-term fluctuations do not bias the annotation strategy.
Finally, a selection rule is applied to dynamically guide annotation. We select the highest-entropy old-class samples and the lowest-entropy new-class samples from the mixed region:
Dold
argmax (Tooa)) U D
R > Riew
Xmax
(15)
Dnow
argmin H (x Dnew,
otherwise,
Znew Exmix
where 'Durl = |Dykl + |D denotes the total annotation budget.
In summary, the proposed reward-guided strategy be interpreted as entropy- information gain maximization mechanism, theoretically reducing adaptation error under limited annotation budgets.
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3.4 BALANCING TRUE-LABELS AND PSEUDO-LABELS OPTIMIZATION
When limited annotated samples are available, relying solely on annotated information for unsu- pervised domain adaptation is impractical. Therefore, pseudo-labels and other information must be used simultaneously. However, improper optimization may cause imbalanced learning, leading to class boundary ambiguity and model collapse. To address this issue, we propose cuistering-basee contrastive loss that enables the model to adaptively learn by balancing the use of pseudo-labels and annotated information.
First, we perform clustering using high-confidence pseudo-labes labell features J, = f(Dur) = ((u: 0 = c}} and annotated features Fa f(Dr)) = {(v,, Bj c)} obtain class prototype Pai
1 F
Σ v.).
(16)
UFEFF,
V.EA.
The cluster enhances the model's robustness and prevents overfitting and class boundary ambiguity that may arise when relying solely on annotation. The contrastive loss should enhance learning by minimizing intra-class distances, clustering pseudo-labeld samples within the same class and reinforcing their attraction to class prototypes. Simultancously, it maximizes inter-class distances by separating known and unknown class samples and ensuring unknown samples remain dispersed and distant from class prototypes to prevent misclassification.
To achieve this, we first extract features of old and new classes, denoted as Fold and Fnew- Next, data augmentation is applied to each sample in both sets, transforming them into feature space and producing Rold and Rnwy. For each augmented sample pair, we associate the corresponding class prototype set P {P )e Map (Fold: Roid, P) and (Fnew: Rnew: P) to low-dimensional projection space to form a unified set of feature representations Q. By calculating the clustering- contrastive loss based on these representations, annotated information can effectively guide high- confidence pseudo-labels, preventing class boundary ambiguity caused by model overfitting:
exp(sip)
Ce
Σ iEIold
log
(17)
S(i)
(E
where Zold and Iuew represent the set of indices of the old and new classes,
S(i)
Σ
exp (Siw) ) + Σ exp()
(18)
()
JEZaia
(1)) = neew UIold represents the negative sample set. Moreover, Sty is the cosine similarity. Finally, the overall objective L for EMAC at the t-th iteration is:
L = LMME + Le-
(19)
EMAC reduces classification errors by selecting appropriate samples for annotation and optimizing the balance between true labels and pseudo-labels for adaptation.
In summary, we first expose mixed sample candidates by modeling the orthogonally decomposed features with GMM to identify regions with the highest annotation validity, thereby avoiding the waste of limited annotation resources. Next, we adopt a reward-guided active selection strategy that quantifies the marginal information gain of each annotated sample with respect to the model's ability separate old and new classes. By explicitly linking the reward entropy reduction and class separability, this strategy ensures that, under limited annotation budget, the most informative and representative samples are prioritized. Finally, we introduce Balancing True-labels and Pseudo-labels clustering-based contrastive loss, which enhances intra-class cohesion
Optimization by designing and inter-class separation. This significantly improves the model's diseriminative ability under label scarcity and alleviates the decision boundary ambiguity caused by limited labeled data.
In Algorithm]] I we first expose mixed sample candidates by modeling the orthogonally decomposed features with GMM to identify regions with the highest annotation validity, thereby avoiding the waste of limited annotation resources. Next, we adopt a reward- guided active selection strategy that quantifies the marginal information gain of each annotated sample with respect to the model's 's ability to separate old and new classes. By explicitly linking the reward to entropy reduction and class separability, this strategy ensures that, under limited annotation budget, the most informative and

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Published as a conference paper at ICLR 2026
Algorithm 1 EMAC
Input: Pre-trained model (a; 0), classifier Wass unlabele d target data Dur-
1: for Each batch of target data DTT do
2: Obtain a batch of features z on (
#Exposing Mixed Sample Candidates
3:
Obtain source-une nurce-unknown features Zunknown using SVD (Eq. equation 3 Obtain candidates Xmix and Xoid, Xnew Zunknown and D using GMM (Eq. equation 7 #Selecting from Candidates
4:
5:
Compute LMMEE on Xnew (Eq. equation 8
6:
Compute Reward Rold, Rnew on LMME (Eq, equation 14
7:
Update annotation DLT on candidates Xmix and conditions Rold, Rold (Eq, equation 15) #Balancing True-labels and Pseudo-labas
8:
Cluster class prototype f.c on DLT and prr (Eq. equation 16 Compute Le on Xold, Nnew and P. (Eq. equation I7) Training and adaptation using LXME and L.
9:
10:
11:
end for
representative samples are prioritized. Finally, introduce Balancing True-labels and Pseudo-labels Optimization by designing clustering-based contrastive loss, which enhances intra-class cohesion and inter-class separation. This significantly improves the model's discriminative ability under label scarcity and alleviates the decision boundary ambiguity caused by limited labeled data.
4 EXPERIMENT
Datasets. We evaluate EMAC on two DA datasets: DomainNet and VisDA-C. DomainNet comprises approximately 0.6 million images spanning 345 classes across three domains: painting (P), real (R), and sketch (S). All 345 classes are present in each domain. We evaluate COMET across all six domain shifts: P-R, P-S,, R-P, R- S PP, and S-R. VisDA-C includes 12 object classes and poses challenging domain shift, transitioning from synthetic 2D renderings of 3D models to real-world images from the Microsoft COCO dataset. Consistent with prior work, we use the first domain the source domain for evaluation.
Evaluation Metric. We adopt the evaluation metrics from OPTTT (Li et al. 2023a), which are designed for open-set classification tasks. UTTA focuses two key aspects: the accuracy of source domain classes and the effectiveness of rejecting private target domain classes. Specifically, Ao measures the accuracy of old target classes, while A evaluates the rejection accuracy of target classes. Their harmonic mean, Ah, offers a balanced assessment of overall predictive performance.
4.1 COMPARISON TEST- TIME ADAPTATION METHODS
We benchmark the following test-time training methods. TEST directly evaluates the source domain model on the test data without adaptation. BN (loffe & Szegedy 2015) updates batch normalization statistics using the test data for test-time adaptation. TIT+ (Sun et al. 2020) minimizes the Frobenius norm between mean covariances to align source and target domain distributions. TENT WWng et al. 2021) adapts the model by updating batch normalization affine parameters through entropy minimization on test data. SHOT (Liang et al. 2020) employs entropy minimization and self- training, assuming a
target domain and encouraging uniform prediction distributions. BiTTA {Lee et al. 2025) introduces binary correctness feedback for test-time adaptation, correcting unstable predictions through lightweight
signals. TTAC (Su et al.) (022) performs distribution alignment at both global and category levels to enhance test-time training. SimATTA (Gui et al. 2024) leverages active learning annotate a small number of target samples, improving model generalization. EATTA (Wang & Ding 2025) proposes an effortless active labeling mechanism aid long-term test-time adaptation by selecting samples based on confidence-driven criteria. OPTTT (Li et al. 2023a) addresses open-world world test-time training by explicitly modeling class shifts for the first time. Finally, introduce EMAC, which integrates active learning with adaptation open-wort world scenarios involving dual shifts. For fair comparison, we maintain consistent pipelines across all baseline methods.
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Published as a conference paper at ICLR 2026
Table Active Universal Test- Time Adaptation results DomainNet. All numbers are in %. (The best options methods are shown bold and indicates the pseudo-update version. The unit of GPU times is seconds.)
Task
Mathod
S2K
SZP
PIR
P2S
RZS
R2F
Avg.
AG
Ax
Ag
Ao
Ax
Ag
App
Ax
Ag
Ao
Ax
Au
Aa
Av
Ag
Ao
Av
Ag
Ai
Sec.
TEST
34.5
78.1
47.8
20.0
76.2
32.0
45.7
77.8
57.6
25.2
$4.4
38.8
25.2
82.5
38.6
35.7
81.1
47.6
43.7
392
TIA
TENT
BN
36.8
16.5
08.4
70.2
47.9
48.0
25.8
24,5
61.9
61.4
36.3
35.2
49.6
48.0
66.3
68.3
56.9
56.4
33.8
30.7
68.1
72.1
43.1
45.2
28.6
31.0
67.4
70.5
40.7
42.5
36.
36.4
7U.2
69.3
47.7
47.6
45.2
46.1
479
577
SHOT
37.5
06.9
48.1
33.9
51.3
40.8
47.5
08.5
56.1
28.0
90.4
42.2
27.8
76.3
40.5
34.4
30.3
48.2
46.0
564
UTTA
OFTTT
TTAC
40.1
45.7
62.8
61.4
48.9
52.4
40.6
40.2
59.6
56.3
47.3
48.0
52.2
36.8
62.6
63.7
51.0
57.4
33.1
31.8
65.2
68.6
42.7
44.6
29.7
34.1
66.2
68.0
41.0
46.1
37.5
38.9
78.3
74.6
49.5
51.9
46.7
49.8
651
697
SIMATTA
41.3
58.6
48.5
36.9
57.1
46.5
49.3
60.1
54.2
28.9
73.1
42.2
31.5
70.5
43.5
35.5
72.6
48.7
47.1
747
ATTA
EATTA
41.5
58.9
48.7
37.8
57.9
45.7
49.9
61.00
54.9
30.1
75.6
43.5
32.4
71.3
44.5
36.4
73.4
48.7
47.7
738
BITTA
37.5
08.4
48.4
27.9
61.7
38.4
69.1
67.9
57.0
31.7
75.2
44.6
32.3
72.8
44.7
37.9
73.6
50.0
47.2
687
AUTTA
EMAC
EMAC
47.7
48.2
06.4
67.2
55.5
56.1
42.3
43.9
57.7
57.3
48.7
49.9
53.5
5388
07.4
05.3
59.8
59.4
36.6
34.9
72.8
71.6
48.7
46.9
35,9
35.2
72.6
72.0
47.9
47.4
41.7
42.3
80.6
79.6
54.7
55.5
52.2
53.1
779
735
Table 2: AUTTA results on VisDA-C.
Table Comparisons to AL baselines AUTTA.
NOISE
MNISTT
DomainNet
S2R
S2P
P2R
P2S
R2S
R2P
Avg.
VisDA-C
Av An
Av
An
Random (B-
1000)
48.2
44.6
53.6
43.9
42.3
48.7
46.8
66.7
TEST
65.1
Eatroyy (B- 1000)
47.6
43.9
52.8
44.6
41.5
47.4
46.3
65.4
TENT
BN
58.8
100.0
98.9
73.8
68.2
82.3
51.0
59.5
81.5
62.7
69.7
Kmeuns (B-
1000)
48.2
44.3
52.1
45.9
41.3
48.6
46,7
66.8
SHOT
62.1
100.0
76.5
48.2
36.0
41.2
26.5
62.8
59.5
(8 1000)
47.5
55.7
50.7
46.4
TIAC
59.2
100.0
74,4
52.0
67.4
98.7
57,9
06.3
61.4
Coreset (B -100))
49.8
48.6
58.3
44.4
47.1
48.6
49,4
68.7
OPTTT
53.9
100.0
77.8
64.2
90.7
75.2
64.5
74.8
69.2
BADGE (8 = 1000)
50.4
47.3
54.6
45.8
46.2
52.4
49.5
68.2
SimATTA
61.1
100.0
75.8
56.7
69.5
62.5
59.5
67.8
63.4
SimATTA
1000)
48.5
44.8
54.2
48.7
47.1
67.9
EATTA
BITTA
62.0
62.8
100.0
100.0
76.5
77.1
51.5
60.2
68.0
78.1
68.0
58.6
63.1
60.3
72.0
74.0
67.3
66.5
EMAC (B 800)
54.5
47.1
58.1
45.4
46.7
53.6
50.8
73.1
EMAC
66.4
101.0
79.8
66.5
92.8
77.4
60.4
752
72.3
EMAC (B 1000)
55.5
48.7
59.4
46.9
47.9
54.7
52.2
76.5
EMAC
67.2
100.0
80.4
66.8
94.7
78.3
70.6
76.1
73.2
EMAC (B 1500)
56.3
48.9
60.8
47.8
48.7
55.6
53.1
78.2
4.2 COMPARISONS BETWEEN EMAC AND THE SOTAS
Noise Corrupted Target Domain. We first evaluate EMAC under noise-corrupted target domains, using painting (P), real (R), and sketch (S) from DomainNet as the source domain and adapting to (R, S, P), respectively. Results in Tab. 18 reveal several trends. Direct testing does always produce the worst performance, as the model is more biased by class shift than by pure domain shift and therefore often predicts unseen classes as unknown rather than collapsing uniformly. TTA baselines (BN, TENT, SHOT, TTAC) improve robustness through distribution alignment but still struggle to disentangle domain shift from new-class uncertainty. OPTTT better handles open-world world conditions via prototype extension, while BiTTA provides binary correction signals but remains too coarse to identify informative samples under universal shift. Active-TTA methods such as SimATTA and EATTA benefit from limited labeling but rely heavily confidence- scoring. which is unreliable when class and domain shifts co-occur, leading to suboptimal querying. EMAC explicitly exposes mixed samples and performs reward- sward-guided querying, achieving the best performance across all settings. Its reinforced variant EMAC further prevents negative updates from sparse annotations. Additional results and GPU-time analysis are provided in Appendix A.3.3
Style Transfer Target Domain, We further evaluate VisDA-C to demonstrate the effectiveness of the style transfer target domain. The results are presented in Tab We draw similar conclusions from the results as with common corruption. Additionally, the number of categories in VisDA-C is much smaller than DomainNet. The results show that all target domain adaptations in VisDA-C outperform those in DomainNet. It can be inferred that when the number of annotated samples far exceeds the number of categories, the imbalance in optimizing limited annotation costs and pseudo-labels will be reduced. Therefore, our proposed method consistently outperforms competing methods both complex and simple open environments.
4.3 ADDITIONAL ANALYSIS
Comparison of Active Learning Methods. To validate the superiority of active learning in AUTTA and to ensure a fair comparison, we wrap all active learning methods (Random, Entropy WWang & Shang 2014), Kmeans, CLUE (Prabhu et al. 2021). Coreset (Sener & Savarese. 2018). BADGE (Ash et al. 2019), SimATTA (Gui et al. (024)) in the TENT framework and compare them with EMAC, ensuring they operate under the same conditions and share the same model backbone.
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Entropy
Entropy
3
3
2
2
UCRS
Random
Kmean
UCRS
Random
Kmean
100
200
300
400
500
600
700
800
100
200
300
400
500
600
700
800
(a) Old classes entropy
(b) New classes entropy
Figure 3: The degree of annotated samples on the model's ability to distinguish between new and old classes.
Cross-Entropy
(b) Mcan Squared Error
(c) Prototype Cluster
(d) Costtr-Conttastive
Figure 4: T-SNE visualization on DomainNet. Black dots are new class examples, while other colors are class examples. The colors indicate different classes.
As shown in Table 3 the results demonstrate that incorporating a limited number of label test instances B significantly improves performance across test domains and class shifts. However, EMAC outperforms or is competitive with other active learning methods due to the higher effectiveness of the labeled samples.
Error Rate in Mix-Gaussian and Entropy. As shown in Table to validate the rationale for introducing GMM and selecting Xmix, we first use the sample entropy (Wang & Shang 2014) method to divide samples into
Table 4: Error rate (%) in gmm and entropy.
Domain
Xaa
EN-x 0.25
Xuew
FN.> 0.75
Xuix
0.25<CNN..0.55
Painting
12.4
15.4
14.3
32.5
77.8
Real
10.2
15.6
11.5
34.6
86.4
71.2
Sketch
11.4
128
13.8
37.1
84.0
68.8
three regions (EN. < 0.25, 0.25 < EN. < 0.75, EN. > 0.75) based on fixed thresholds (0.25 and 0.75), simulating GMM's segmentation. Next, We randomly select 1,000 samples from the three regions divided using the EMSC and entropy methods in target domains (Painting, Real, Sketch), and compare the predictions with the ground truth. The results indicate that with the EMSC method, the error rate in Xmix is significantly higher, while the emrrr rates in Xold and X remain negligible. In contrast, the entropy method shows non-negligible error rates across all regions.
Visualization Analysis. To demonstrate the representation adaptation effectiveness of different loss functions, we utilize t-SNE (der Maaten Hinton 2008) to reduce feature dimensionality for visualization. Fig. 4 compare commonly used loss functions, including cross-entropy loss, mean squared error loss, prototype learning loss, and Conttar-o (OURS). Cluster-Contrastive achieves more distinct separation between old classes (colored) and new classes (black) samples. Additionally. we observe that it enhances the separation within old class samples, highlighting its effectiveness in improving feature representation. These results demonstrate that cluster-contraste loss C. effectively balances the use of scarce annotated information and pseudo-labels, enabling the model overcome class boundary ambiguity and achieve better training and adaptation.
Sensitivity to Small Batch Sizes. We investigate the sensitivity of EMAC to small batch sizes 8). For such small batches, naive per-batch computation often becomes unstable during GMM fitting, leading to noisy uncertainty estimation. To address this, adopt sliding window buffer and Exponential Moving Average(EMA) smoothing. The sliding window buffer accumulates data over multiple iterations, providing more reliable GMM fitting, while EMA smooths uncertainty estimates by considering both current and past values, reducing short-term fluctuations. The results in Table show significant performance gain the S2R (DomainNet) dataset after applying these techniques,

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Table 3: Sensitivity to Small Batch Sizes.
Table 6: Ablation study.
Batch Size
Method
Ao
Ax
DomainNet
VisDA-C
EMSC SC
BTPO
EMAC (naive)
12.5
16.3
Ao
Ax
Ag
Ao
A
EMAC (w/ window)
39.0
60.1
47.3
30.1
80.2
43.7
54.3
81.8
65.0
4
EMAC (naive)
19.5
32.3
20.1
4
41.0
54.9
47.1
60.1
82.3
69.4
4
EMAC (w/ window)
40.1
61.9
48.6
7
V
42.5
43.6
54.7
56.3
49.1
61.4
61.8
84.6
85.1
71.7
71.2
8
EMAC (naive)
27.8
39.5
32.5
v
44.8
58.9
50.8
64.0
73.1
8
EMAC (w/ window)
41.3
62.7
49.1
V
45.5
61.2
52.2
66.9
89.3
76.5
demonstrating their effectiveness in stabilizing performance under small batch sizes. Full details are provided in the AppendixA.3.7
Ablation Study. We analyze the effectiveness of the three components: Exposing Mixed Sample Candidates (EMSC), Using Selecting from Candidates (SC), and Balancing True-labels and Pseudo- labels Optimization (BTPO), as detailed in Tab When used individually, each component shows limited effectiveness. For example. SC depends on EMSC to identify annotating regions, ensuring labeled sample validity, while BTPO relies on annotated labels to update class prototypes and prevent boundary blurring. SC leverages max-min entropy method to refine boundaries between new and old classes by selecting nigh-coontibution samples. Meanwhile, BTPO strengthens boundaries among old classes and further separates new and old classes by updating prototypes. Additionally, BTPO enhances EMSC's discrimination ability, improving the quality of samples for SC and ensuring stable, meaningful prototype updates. This synergy forms positive feedback loop, progressively boosting model performance and adaptability, Further Additional Experiments Analysis in the appendix A.3
5 CONCLUSION
This paper presents Active Universal Test-Time Adaptation (AUTTA), novel framework that effectively handles both domain and class shifts with minimal human annotation. By integrating active learning and universal test-time adaptation, AUTTA improves model adaptability in dynamic environments. The core innovation, EMAC, uses Gaussian Mixture Models (GMM) for sample selection and reward- reward-guided strategy to optimize annotation efficiency. Experiments on DomainNet and VisDA-C show that AUTTA outperforms existing methods, demonstrating its effectiveness in dual-shift scenarios. While promising, AUTTA still relies on human annotations, which can be limitation in certain applications. In particular, for fine- fine-grained tasks, such as distinguishing between butterfly species, expert-level annotations may be required, which could become bottleneck. Future work should focus on reducing human intervention and improving the model's 's performance in more complex environments, making it more scalable and applicable to broader range of tasks.
ACKNOWLEDGGENNS
Our work was supported by the following institutions and projects: National Natural Science Founda- tion of China (Grant No. 62572349, 12476189, 62406323); Suzhou Science and Technology Project (No. SYG2024149); Emerging Frontiers Cultivation Program of Tianjin University Interdisciplinary Center.
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A APPENDIX
A.I POTENTIAL PROBLEMS WITH THE ARTICLE
Limitations of application scenarios (eg, AUTTA of High-stakes environments). In high-stakes domains such as medical AI, acquiring annotated data can be extremely costly, and due to strict time constraints, even limited human intervention may be impractical. However, many real-world applications still require high test-time robustness while allowing for low-latency human-in-the-loog adaptation. Recent research [1] has increasingly focused interactive, low-latency systems capable of operating in dynamic environments. Our proposed AUTTA framework is specifically designed to meet these needs by minimizing reliance on manual annotations while enabling rapid model adaptation. This makes it particularly suitable for scenarios such as smart home control and campus autonomous driving. For example, in campus driving system, AUTTA can quickly adapt to changing road conditions and pedestrian behaviors without requiring offline labeled datasets.
Differences from open-set AL and main contribution. While the AUTTA task shares similarities with open-set active learning (AL), fundamental difference lies in its focus on the test-time phase. Specifically, AUTTA requires the model to adapt and make real-time predictions without access to the source data, which is typically available in traditional active learning settings. Our main contributions are twofold: (1) we introduce the AUTTA paradigm, a novel framework tailored for adaptive learning in dynamic environments with minimal annotation; and (2) propose new active learning strategy designed to ensure the informativeness and utility of queried samples under AUTTA constraints. Our approach effectively balances annotation cost with rapid model adaptability, addressing the critical demands of real-world applications such as robotics, smart environments, and autonomous systems.
Inconsistency in Max-Min Entropy Objective and Sample Selection? The Max-Min Entropy objective and the sample selection strategy serve different purposes and are not directly coupled. The Max-Min Entropy objective aims to shape the model's internal representation by encouraging separation between known and novel classes, while the annotation strategy selects uncertain samples to improve decision boundaries. Although these components operate independently, they complement each other to enhance the overall performance of the system. Max-Min Entropy Objective: This objective is designed to enhance class separability by adjusting the model's confidence scores. Specifically, it reduces entropy for samples that likely belong to known classes, and increases entropy for samples that likely belong to novel classes. This contrast in treatment helps the model form clearer distinction between old and new categories. Sample Selection for Annotation: Our annotation policy focuses on selecting the most ambiguous samples from the candidate region identified by the GMM-based exposure module. These typically samples with uncertainty levels that fall between those of typical old and new class instances. As a result, the selected samples tend to include high-entropy old-class samples and low-entropy new-elass samples. These samples difficult for the model to distinguish and are therefore the most informative for annotation.
Comparison of Test-Time Adaptation Paradigms. As summarized in Table 7 our proposed AUTTA framework differs from existing test-time adaptation paradigms in several key aspects. Unlike traditional TTA, which typically relies on source data, AUTTA operates in a source-free setting. Compared to UTTA, which is entirely unsupervised, AUTTA incorporates active sample selection and minimal human-in in-the-loop supervision to improve adaptation efficiency, Moreover, in contrast to ATTA, which may not prioritize real-time constraints, AUTTA is explicitly designed for rapid adaptation in dynamic environments with minimal annotation cost.
Table 7: Key Differences Between TTA, UTTA, ATTA, and the Proposed AUTTA
Feature
TTA
UTTA
ATTA
AUTTA
Test-time adaptation
Requires source data
Active sample selection
Human-in-the-llog, labeling
Unsupervised adaptation
Suited for dynamic environments
Partial
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Why UTTA Requires Active Learning (Theoretical Justification)? UTTA relies solely supervised adaptation mechanisms, adjusting the model using unlabeled test data without external feedback. This often causes the model to get trapped in local optima accumulate adaptation erros, especially in dynamic environments with continuously changing distributions. To address this, integrating Active Learning (AL.) by selectively querying labels for the most informative samples significantly enhance adaptation effectiveness.
Recent theoretical frameworks developed for ATTA al. 2024) rigorously demonstrate that incorporating a limited amount of labeled samples can effectively reduce the generalization and adaptation error bounds of the model. Specifically, AL contributes to:
(1) Improved sample efficiency: Actively selecting the most valuable samples for model improvement avoids random or redundant labeling, enhancing learning efficiency.
(2) Faster model convergence: Feedback from high-quality labeled samples enables the model to quickly correct erroneous predictions during test time, mitigating error propagation.
(3) Enhanced robustness and generalization: Active sampling helps the model cover a broader variation of the input space, improving its ability adapt unseen environments.
Therefore, combining AL strategies within UTTA enables faster and more effective test-time adapta- tion under source-free constraints with minimal annotation cost. Our AUTTA framework builds upon and extends these theoretical benefits from ATTA by designing an optimized sampling strategy that maximizes the contribution of labeled samples adaptation performance.
Defective human annotations. We introduced pseudo-labels containing controlled annotation errors 55% error rate) during the training process. The results demonstrated only a 2% decline in model testing accuracy, We initially attributed this to the online features of TTA, and we will add metrics in the revised version. see Table
Table 8: Key Differences Between TTA, UTTA, ATTA, and the Proposed AUTTA
DomainNet
S2R
S2P
P2R
P2S
R2S
R2P
Avg.
VisDA-C
Random (B 1000)
48.2
44.6
53.6
43.9
42.3
48.7
46.8
66.7
Entropy (B 1000)
47.6
43.9
52.8
44.6
41.5
47.4
46.3
65.4
Kmeans (B = 1000)
48.2
44.3
52.1
45.9
41.3
48.6
46.7
66.8
CLUE (B 1000)
44.6
47.5
55.7
40.3
39.8
50.7
46.4
67.3
Coreset (B 1000)
49.8
48.6
58.3
44.4
47.1
48.6
49.4
68.7
BADGE (B 1000)
50.4
47.3
54.6
45.8
46.2
52.4
49.5
68.2
SimATTA (B = 1000)
48.5
44.8
54.2
42,2
43.5
48.7
47.1
67.9
EMAC (B 800 5
53.5
46.4
57.4
44.9
45.9
52.9
49.9
72.4
EMAC (B (00/error8%)
53.1
47.0
56.6
44.0
45.2
52.1
49.3
71.5
EMAC (B 800/error /errorr 10%)
52.1
45.2
56.0
43.4
44.6
51.6
48.7
69.0
A.2 DISCUSSION ON GMM ASSUMPTIONS
Our method relies Gaussian Mixture Models (GMM) expose mixed regions within a mini-batch. By design, G.M performs best when the batch distribution exhibits multi-modal (especially bimodal) patterns, which correspond to the coexistence of old- and new-class samples. A natural concern is whether the method would fail when batch does not clearly present such bimodality. We address this issue follows.
First, when the batch is dominated by old-class samples, the distribution tends to concentrate in low-uncertainty regions. In this case, the model is confident in its predictions and requires no additional annotations. Second, when new-class samples dominate, the distribution shifts toward high-uncertainty values. Although a typical bimodal pattern may not emerge, the system still updates its representations through entropy-based and contrastive objectives, thereby improving adaptation to new classes. Third, when old and new samples are severely confused due to strong domain overlap, the uncertainty distribution approximates flat unimodal shape. In this case, GMM conservatively suppresses candidate exposure, preventing annotation budget waste on low- value samples and maintaining stability under ambiguous conditions. In all these situations, the
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system adopts conservative strategy that minimizes candidate regions, ensuring robustness without unnecessary budget consumption.
Subspace Projection before GMM To mitigate instability in high-dimensional feature space, first project features into a more stable subspace before fitting GMM. Given a batch of features X Rxxd PCA computes projection matrix W dRdk (k: < d) such that:
Z = XW,
W arg max W W=T
Tr(W1 SW),
(20)
where S = E (z; (24 n) is the covariance matrix. Alternatively, when using an autoencoder, the encoder so(:) provides bottleneck representation:
Z = fx)), Z R
(21)
which preserves semantic structure while reducing dimensionality. The projected features Z are then modeled by GMM, improving robustness against high-dimensional noise.
Variational Bayesian GMM (VBGMM) To avoid the need for pre-specifying the number of components, we further adopt a Variational Bayesian GMM (VBGMM). Unlike standard GMM, VBGMM introduces Dirichlet prior over the mixture weights m.
Dir(oo), 2, Categorical (T), I; ~ NN Ex,);
(22)
where og is the concentration parameter. Variational inference then maximizes the evidence lower bound (ELBO):
LELBO Eollog 1,,
M4, - E Eo ((Z 14, E)},
(23)
leading automatic model selection where redundant components are pruned during inference. This Bayesian formulation reduces overfitting and increases stability under small-batch or noisy conditions.
Empirical Verification Across six benchmark datasets, we observe that when the batch size is greater than 32, the success rate exposing bimodal regions using the combined trick (Subspace Projection + VBGMM) reaches 93%, confirming the practicality of this approach.
A.3 ADDITIONAL EXPERIMENTS ANALYSIS
A.3.1 ROBUSTNESS UNDER NON-LLLD, AND CONTINUAL SHIFT
To further examine the robustness of AUTTA under realistic and challenging scenarios, we conduct additional experiments involving non-i.i.d. test streams and continual distribution shifts. These settings simulate bursty class arrivals, class imbalance, and sequential domain shifts, which are common practical deployments such as autonomous driving and online learning.
Class Ordered Stream (Non-i.i.d.). We first evaluate AUTTA under a class-ordered stream on DomainNet (S R), where only 1-2 classes appear each batch. This simulates bursty class arrivals and strong class imbalance. Results are shown in Table Compared with baselines, AUTTA achieves both the highest harmonic mean accuracy and the lowest reward variance, confirming its robustness and stability under skewed test streams.
Table Performance under Class Ordered Stream (Non-i.i.d.) on DomainNet (S + R). AUTTA achieves the lowest reward variance.
Method
Ao
Ax t
Aи 1
Reward Var. J
TENT
34.0
63.5
45.7
0.051
SHOT
34.8
61.9
45.9
0,053
TTAC
37.2
58.1
46.5
0.043
ОРTTT
42.5
56.3
49.0
0,036
SimATTA
38.1
54.4
44.9
0.039
EMAC (Ours)
44.0
60.5
51.3
0.015
Continual Shift (CTTA: S + R P). Next, we validate robustness under continual shift scenario (CTTA), where the model sequentially adapts from Sketch
Real Painting without reset.
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This setting evaluates the method's ability to maintain stability and low reward variance under long-horizon, evolving distributions. Results in Table [0 demonstrate that AUTTA consistently outperforms competing methods, achieving both higher average accuracy and the lowest reward variance across domains.
Table 10: Performance under Continual Test-Time Adaptation (CTTA) on DomainNet (S R AUTTA
yields the lowest reward variance.
Method
R-Ao
R-Ax
R-A
P-Ao
P-AN
P-Ag
AVG-Ao
AVG-Aş
Reward Var. +
TENT
42.1
56.5
48.3
26.4
45.2
33.3
34.3
40.8
0.057
SHOT
43.4
57.1
49.3
28.1
46.7
35.0
35.8
42.2
0,061
TTAC
45.2
58.0
50.9
29.8
48.5
36.5
37.5
43.7
0.046
OPTTT
48.3
58.9
53.1
31.2
49.6
37.4
39.8
45.8
0.038
SimATTA
51.1
30.6
49.0
38.3
44.3
0.041
EMAC (Ours)
50.2
60.1
54.7
34.0
52.2
42.1
48.0
0.018
These experiments highlight AUTTA's robustness under highly non-stationary test environments. By combining entropy smoothing with GMM- based exposure, method maintains stable reward signals and effectively allocates annotations, achieving both higher accuracy and lower reward variance than baselines. This confirms its practical applicability in real-world online adaptation tasks.
A.3.2 EFFECTIVENESS UNDER SINGLE SHIFT CONDITIONS
Although AUTTA is primarily designed for the more challenging dual-shift setting (simultancous class and domain shifts), also evaluate its generalizability under simplified single-shift scenarios, where only class shift or domain shift is present. These settings represent degenerate cases of AUTTA, and our results show that the method naturally adapts with minimal modifications.
Adaptation Strategy, For single-shift scenarios, AUTTA can be simplified as follows: (i) treat the entire batch as the candidate pool instead of fitting GMM, since ambiguity is less structured; (ii) rank samples purely by entropy, prioritizing those with higher uncertainty; (iii) focus optimization only on uncertain samples, novelty arises from either class domain shift, but not both simultaneously. This maintains the label-efficient principle of AUTTA while ensuring stability.
Table 11: Performance of AUTTA under single-shift scenarios DomainNet. Results show that AUTTA adapts effectively even degenerate cases, outperforming the dual-shift baseline.
Setting
Ao
A f
Aи t
Notes
Dual Shift
48.2
52.7
50.4
Full AUTTA (class domain shift)
Only Class Shift
53.3
55.9
54.4
New classes only, fixed domain Same classes, transferred domain
Only Domain Shift
57.4
As shown in Table 11 AUTTA achieves superior performance under single-shift conditions compared to the dual-shift baseline. the class shift setting, entropy-based annotation effectively captures novel semantics, boosting recognition of unseen categories. In the domain shift setting, entropy smoothing and contrastive learning stabilize adaptation across domains, leading to a large gain Ao- These results confirm that AUTTA remains highly effective and introduces bias or false positives, even under simplified conditions.
A.3.3 TIME COSTS AND COMPUTATIONAL OVERHEAD
We further analyze the runtime overhead of AUTTA to assess its practicality in real-world test- time adaptation, A full adaptation cycle was profiled on ~84000 test images, with the breakdown summarized in Table 12
Runtime Breakdown. As shown in Table 12 (71.7%), which is expected since fine-tuming and backpropagation dominate in test-time learning pipelines. contrast, the combined cost of exposure, reward estimation, and sample selection accounts for less than 30% of the total runtime. Notably, sample exposure and annotation require
the majority runtime spent on model adaptation
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only 3-6% of samples per iteration, making the annotation process efficient and computationally lightweight.
Table 12: Runtime breakdown of AUTTA across all domains.
Stage
Description
Time (s)
% of Total
Exposure Module
SVD decomposition and GMM fitting Z unknown
95
12.9%
Reward Computation
Entropy calculation EMA smoothing for reward estimation Reward-guided guided selection of high-utility samples Contrastive learning with pseudo-labels and anchors
72
9.8%
Sample Selection
41
5.6%
Model Adaptation
527
71.7%
Total
735
100%
Comparison to Baselines. We also compare the total runtime and average accuracy with standard
UTTA methods (Table [3]. While AUTTA incurs slightly higher runtime (735s) due to structured
uncertainty modeling and adaptive annotation, it achieves the best overall accuracy (52.2%) This demonstrates a favorable trade-off between accuracy and efficiency in dual-shift AUTTA scenarios.
Table 13: Comparison of average accuracy (Axx and runtime with baselines on DomainNet.
Method
Avg. Accuracy (Aw 1)
Runtime (s 4)
TEST
43.7
392
BN TENT
44.3
577
45.2
SHOT
46.1
564
TTAC
51.1
653
OTTTA
51.6
697
SimATTA
51.0
536
AUTTA (Ours)
52.2
735
Despite a moderate increase in runtime, AUTTA provides the highest accuracy among all compared methods. The structured design ensures that computational costs remain manageable, while the performance gain justifies the overhead. This highlights the practicality of AUTTA in real-world scenarios, where accuracy-efficiency trade-offs are critical.
A.3.4 ROBUSTNESS UNDER LARGE DOMAIN GAPS AND EXTREME SHIFTS
To further evaluate the robustness of AUTTA, investigate two critical aspects: (i) adaptation under large domain gaps, and (ii) the reliability of GMM modeling under extreme shifts and complex multi-class scenarios.
Adaptation under Large Domain Gaps. While DomainNet and VisDA-C already introduce substantial inter-domain variations, we extend the evaluation to the Office-Home dataset, which includes four distinct domains: Art (A), Clipart (C), Product (P), and Real-Worlds (R). These domains present significant style and appearance variations, and we test AUTTA on two representative transfer settings: A-RR (hand-drawn real images, semantic and visual gap) and C-+P (cartoon icons to product photographs, shape and texture gap). As shown in Table I4 AUTTA consistently outperforms strong baselines, particularly the domain gap becomes
severe.
Robustness of GMM Modeling under Extreme Shifts. Since AUTTA relies on GMM modeling to identify mixed regions, evaluate its stability under diverse benchmarks and extreme shifts using the Bhattacharyya Distance (BD). batch is considered to exhibit reliable bimodal separation if BD)
Table 14: Performance Office- Home under AUTTA setup. Results demonstrate robustness of AUTTA under moderate-to-lane domain gaps.
Method
A- Ao
A Av
A An
C-P Ao
C-P Ax
C-PP A
Avg
TTAC
44.5
56.8
50.0
41.3
54.3
47.0
48.5
ОPTTT
46.3
58.1
51.5
42.6
49.9 55.0
48.2
AUTTA (Ours)
49.4
61.2
54.6
45.8
58.0
51.2
52,9
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Table 15: Bhattacharyya Distance (BD) analysis for GMM modeling under extreme shifts. A higher percentage batches with BD indicates more stable bimodal separation.
Dataset
Scenario
Batches with BD 1.2
DomainNet
Complex multi-class (345 classes)
89%
VisDA-C
Extreme domain shift
95%
CIFARIO-C
Moderate complexity
97%
CIFAR100-C High class complexity
91%
ImageNet-C Complex multi-class
87%
Office-Home
Severe domain shift
92%
Table 16: Performance of different methods the low-separation (bottom 20%) batch subset DomainNet. All numbers are in Avg.%.
Task
Method
Ao
Ax
Aи
TEST
35.2
66.0
40.3
BN
36.0
64.5
40.2
TTA
TENT
36.4
64.0
40.0
SHOT
36.8
65.2
41.0
TTAC
38.5
64.8
42.5
UTTA
OPTTT
40.2
63.7
44.2
SimATTA
37.5
64.1
42.0
ATTA
EATTA
37.9
64.5
42.3
BiTTA
37.8
64.9
42.8
AUTTA
EMAC
42.6
64.9
46.3
1.2. As summarized Table 15 the vast majority of batches across all datasets- dattasts-innludding those with severe domain gaps high class diversity-satisey this criterion, confirming the robustness of the GMM-based exposure mechanism.
These results confirm that AUTTA maintains state-of-the of-te-r--t performance across both moderate and severe domain gaps, and that GMM modeling remains reliable even under extreme shifts and complex multi-class conditions. This highlights the robustness of our framework in challenging real-world adaptation scenarios.
Behavior Low-separation Batches. The BD analysis above shows that most test-time batches admit a clear bimodal structure. To directly address the reviewer's 's concern about highly overlapping or weakly bimodal new-class batches, we further analyze the "worst-case" regime on DomainNet. Concretely, we: (i) compute for each test-time batch simple normalized mean-difference between the two GMM components as separation score; (ii) sort all batches by this score and select the bottom 20% with the lowest separation, forming a hard subset that emulates the reviewer's 's scenario where bimodality is weak or absent; and (iii) re-evaluate all TTAUTTTA/ATTA methods, including EMAC, on this subset.
Table 16 reports the performance of different methods on this low-separation subset. As expected, the performance drops for all methods due to the increased difficulty. However, EMAC consistently remains the persforrformnn method and exhibits graceful degradation rather than failure this low-separation regime. This supports our claim that, even when the mixture structure is weak and batches are highly overlapping, EMAC comparatively robust and does not collapse.
A.3.5 UNDER DIFFERENT OPEN- WORLD DATA RATIOS
In practical applications, the ratio of new and old class data is variable. We investigate the impact of different ratios between new and old class samples on model performance. In the open-world test data, there three scenarios for the ratio of new class data: first, the new class is absent, containing
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only domain shift; second, the old class categories in the target domain only partially present in the source domain; and third, the old class categories in the target
domain are fully contained within the old class categories of the source do- main. Specific parameter settings are provided in Table 17 The main text focuses on the second, most common scenario (partial old classes). We also conduct experiments for the other sce- nario (see Table [8p. The experimen- tal results show that our method not sensitive to data ratios, achieves SOTA performance, and can be applied to various data ratio scenarios.
Table 17: Class splits
datasets, i.e. number of the old and new classes, for the three category shift scenarios New-None, New- partially and Old-fully, respectively.
Ratios Old, New
New-None
New-partially
Old-fully
DomainNet VisDA-C
345, 0
200, 145
245, 100
12, 0
6, 6
9,3
Table 18: All numbers are in % for the old classes fully scenario on DomainNet. All numbers are in %. (The best options in methods are shown in bold and indicates the pseudo-update version.)
Task
Method
S2R
S2P
P2R
P2S
R2S
Avg
Ao
Av
An
Ao
Áv
Ag
Ao
Ax
Ag
Ao
Ax
Ag
Ao
Ax
Ag
Ao
Ay
Ar
Ay
Sec.
TEST
33.4
77.0
46.7
18.9
75.1
30.9
44.6
56.5
21.1
83,3
37.7
24.1
81.4
57.5
32.6
80.0
46.5
42.6
390
TTA
BN
TENT
35.7
35.4
67.3
69.1
46.9
23.5
24.7
60.8
34.1
35.2
48.5
46.9
67.2
55.8
55.3
29.6
67.0
71.0
42.0
44.1
27.5
69.4
66.3
41.4
39.6
35.3
35,0
68.2
46.5
46.6
44,1
575
SHOT
36.4
65.8
47.0
32.8
50.8
39.7
46.4
67.4
55.0
27.5
79.3
41.1
26.7
75.2
39.7
33,3
79.2
47.1
44,9
562
TTAC
39.0
61.7
47.8
39.5
55.2
16.1
35.7
81.5
19.9
30.7
64.1
41.6
28.6
65.1
39,9
36.4
73.5
48.8
45.6
649
UTTA
OPTTT
44.6
60.3
51.3
39,1
58.5
46.9
51.1
62.6
56.3
32.0
67.5
43.5
33.0
66.9
45.0
37.8
77.2
50.8
48.7
695
ATTA
SinATTA
40.2
57.5
47.4
35,8
56.0
43.7
48.2
59.0
53.1
27.8
77.0
41.1
30.4
69.4
42.4
34
71.5
47.6
46.0
745
AUTTA
EMAC
46.6
65.3
51.4
41.2
56.2
47.6
52.4
65.7
58.3
33.8
70.5
45.8
34,8
70.9
46.8
40.6
78.5
53.6
51.1
733
EMAC
71.7
54.4
A.3.6 IMAGENET DATASET AND EXPERIMENTAL SETUP
In this work, we evaluate our model on the ImageNet-C dataset, specifically focusing on four types of corruptions: Noise, Blur, Weather and Digital. ImageNet-C is well-known benchmark designed to test the robustness of models to common image corruptions that occur in real-world scenarios. The ImageNet-C dataset consists of 1,000 classes, which are the same as the original ImageNet dataset. These categories split into two groups based their novelty the model: 600 old classes (previously seen during pre-training and 400 new classes (those that were not included in the original training data). This division reflects typical domain adaptation scenario where models must generalize from previously seen categories to unseen ones, mimicking real-world distribution shifts. is important to note that we use about 100,000 images from the ImageNet-C test for evaluation. These images have been corrupted with the aforementioned types of distortions, providing challenging scenario for model testing and comparison. This setup allows us to evaluate the robustness of our model to real-world image distortions and to assess the effectiveness of domain adaptation and test-time adaptation techniques.
Table 19: Active Universal Test-Time Adaptation results on ImageNet-C. All numbers are in %.
Noise
Blur
Weather
Digital
Avg.
Task
Method
Ao
An
Ay
Ao
Ay
Aj
Ag
Ax
A
Ao
Ay
Ay
Az
Sec.
TEST
28.1
63.7
45,9
16.3
62.1
39.2
37.2
63.5
48.0
20.5
68.9
33.7
35.7
332
BN
29.7
57.3
41.7
20.0
50.5
35.5
39.2
56.5
48.0
25.0
58.8
37.5
36.8
492
TTA
TENT
30.0
40.8
21.0
40.4
54.4
46.9
56.5
38.9
406
SHOT
30.6
54.7
41.6
27.6
41.7
34.9
38.7
55.9
48.8
23.3
65.5
30.3
37.5
480
TTAC
32.7
51.1
41.9
33.1
45.9
39.4
30.0
67.2
42,4
25.9
$3.2
37.5
38.0
553
UTTA
OPTTT
37.2
50.4
43.5
32.9
48.7
39.4
42.9
52.1
47.5
27.1
56.0
38.5
40.6
611
SiMATTA
33.7
47.3
40.5
30.1
46.5
38.1
42.0
49.0
45.9
23.6
63.7
31.9
37.8
670
ATTA
33,9
48.0
40,9
30.9
47.1
38.6
41.6
49.9
45.6
26.0
64.5
31.8
37.7
655
BITTA
30.6
56.2
43.4
22.8
50.0
36.6
40.7
55.7
46.4
25.9
62.2
38.8
37.8
634
AUTTA
EMAC
39.5
56.3
47.9
34.2
50.2
42.2
45.8
58.9
$1.6
28.6
60.2
42.1
40.6
694
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A.3.7 SENSITIVITY TO SMALL BATCH SIZES
In this section, we investigate the sensitivity of the EMAC framework small batch sizes (5 8). Small batch sizes often result in unstable per- batch computations during Gaussian Mixture Model (GMM) fitting, leading to noisy uncertainty estimates. To mitigate this issue and stabilize the performance, we introduce two techniques: sliding window buffer and Exponential Moving Average (EMA) smoothing.
Sliding Window Buffer The sliding window buffer accumulates data over multiple iterations, allowing for more reliable GMM fitting. Instead of fitting the GMM on single small batch, the sliding window collects data from multiple consecutive batches, providing more stable and representative sample distribution. The process is as follows:
Buffer( [) = { BB1, B2,
By}
(24)
At each iteration, the new batch B, is added to the buffer, and the oldest hatch is removed the buffer size exceeds N:
B; B B;
(25)
Once the buffer is updated, the GMM is fitted using the samples in the buffer:
Fit GMM on B (using the collected batches in the buffer)
(26)
Exponential Moving Average (EMA) Smoothing EMA smoothing is applied the uncertainty estimates from the GMM fitting process. This technique smooths the uncertainty estimates by considering both the current and past values, reducing short-term fluctuations. The update rule for EMA is given by:
U, a a-=-UU-11 (1-1++11-0 -
(27)
where: U, is the smoothed uncertainty estimate at iteration t, U--1 is the previous uncertainty estimate,- UUra is the raw uncertainty estimate from the current iteration, - a is the smoothing factor (typically close to 1 for slow changes).
This process helps reduce short-term fluctuations in the uncertainty estimates, leading to a more stable performance.
A.4 DETAIL. OF EVALUATION METRIC
We propose evaluation metric inspired by OPTTT (Li et al. 2023a), designed for tasks similar to open-set classification. The UTTA evaluation focuses on two aspects: the accuracy source domain classes and the effectiveness of rejecting private target domain classes. To quantify these, we define Ao to measure the classification accuracy for target domain old classes and target new classes Accuracy (An) to assess the rejection accuracy for private classes the target domain. Since balancing these two metrics is crucial in this binary task, we calculate their harmonic mean, denoted as A Ah provide a comprehensive and balanced prediction performance evaluation:
Ao Em.lE, 1(v: Ú:) 1(ys E C.)
EXMED. 1(m E C.)
Σ Ex.DED, 1(0: E C./C,) 1( E C./C.) EXMEDD 1(y C./C.)
Ay =
(28)
Ao.AN
Aн 2 Ao + AN
where we use C₂ and C, to represent the label set of the source domain and the target domain, respectively.
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foldd
Model
?
Proj()
Fenew
c
Prototype
Pc
Rold
Model
Proj()
Rnew
Figure 5: Clustering-Contrasie Loss.
Figure 6: The trend of entropy during the iteration process.
Figure 7: Training costs comparisons. "GPU Mem." denotes the training requirement, and "Ace." is the Avg. scores (%).
Entrops Reduction &x OK and New Class Aunctation
Mehvol
NCISI:
MNIST
Ag
.A0
Ag
-A
AA
Au
186L8
VIAE
100.8
75.3
52.5
05.2
51.5
35.7
63.1
GPTTI
$4.7
100.8
78.5
65.0
91.5
76.0
65.3
15.0
750
SIAATIA
51.9
100.8
76.5
57.
01.3
61.5
803
66.0
64.2
42
EMAC
57.2
100.8
50.5
07.3
03.6
78.2
70.2
75.0
73.1
EMAC"
100.
81.3
07.6
05.5
79.1
71.4
75.
74.
A.5 ELLSSTERING-CONTRASTTIN LOSS
In this section, we provide a detailed explanation of the specific processes Clustering Contrastive Loss (see Fig. 5). specifically, first extract the features of old classes from x denoted as Fold, Similarly, pseudo-labels of new classes from new denoted as Fuew, are obtained. Sec- ond, we apply data augmentation each sample in rold and new and transform them into the feature space, resulting in two sets, Rold and Ruew, Next, we extend each augmented sample pairs (Fold/new Rold/new } in the set by adding the class prototype p. that matches their respective pscudo-labels. Finally, we both Rold and Ree all its feature representations to a low-dimensional projection space. The
multi-layer perceptron with single hidden layer Proj(-) to map
result 1,,% 13N5M +3N! = (roj(r:)le Ir E Rold Ruew, where 3.Vold is represented as elements
indexed within the index set Iold = {1, .l., 3.ooldy corresponding to the augmented prototype triplets of features old classes samples. Therefore, the 3N index is included in the index set Inew = { + 1,.., 1,,.,,2N 2. corresponding to the augmented pairs of features new class samples. By calculating the clustering-contrastive loss based on these representations, annotated information can effectively guide high-confidence sseudo-labele preventing class boundary ambiguity caused by model overfitting:
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A.6 USER STUDY ON ANNOTABILITY OF MIXED SAMPLES
To further evaluate the practical usability of the annotated samples selected by our method, we conducted scall-scale user study. We randomly sampled 200 images from the Sketch (S) domain and asked 8 human participants to independently label 20 images they found ambiguous but still meaningful under the target task. This led a human-selected pool of unique images, which were considered "hard but informative" to annotate.
We then applied three different sample selection strategies: our proposed EMAC strategy, entropy- based sampling, and random selection, and compared the overlap between each method's 's selected samples and the numan-selecter pool. Additionally, we asked participants to attempt labeling each selected sample to assess label agreement and annotability. The results, summarized in the following table, show that the EMAC strategy significantly outperforms other strategies in terms of both labelability and human model overlap:
Method
% of Samples Deemed Labelable by Humans
Human Model Overlap (%)
Avg. Human Label Agreement (%)
(Ours)
X3.6%
91.2%
Satropy--asser
68.5%
36.3%
87.0%
Random
61.2%
25.7%
85.4%
Table 20: Results from user study on labelability and human model overlap for different annotation strategies.
% of Samples Deemed Labelable by Humans: This metric quantifies the proportion of selected samples that human annotators consider ambiguous yet clearly labelable under real-time constraints, indicating the practical usability of the exposed samples.
Human-Model Overlap (%): This measures the percentage of model-selected samples that overlap with the human-selected pool of 57 samples. A higher overlap indicates that the model's sample selection aligns well with human annotators' perceptions informative and uncertain regions.
Average Human Label Agreement (%): This measures the average inter-annotator agree- ment, capturing the consistency of labeling between multiple human annotators. A higher agreement suggests that the selected samples, although challenging, are well-defined and stable for human labeling.
The results indicate that our EMAC strategy consistently selects samples that are not only more informative in terms of model improvement but also more accessible for human annotators. The higher human model overlap and labelability suggest that our GMM-based exposure aligns well with human uncertainty perception, while avoiding the selection of samples with ambiguous or uninformative noise. This study validates that our method not only improves model performance but also ensures that the selected samples are practically useful for human annotators.
A.7 REWARD FACTOR
The purpose of the reward factor is assess the weight contribution of annotated samples in the model optimization process. For example, annotated old class samples not only contribute to the model's learning of the old class but also play role in learning new class knowledge, although with different weights. To observe the weight proportions of different samples, we observe the changes in the model's optimization objective function (loss function) when using only old class annotated samples and new class annotated samples, shown in Figure Experimental results show that when only using old class annotated samples, the entropy reduction the samples in the model's y approximately three times the entropy reduction when only using new class annotated samples.
A.8 ABLATION ON
BALANCING MECHANISM
Motivation. BiTTA emphasizes the importance of balancing the influence between labeled feedback and unlabeled predictions during adaptation. Although BiTTA itself is not designed for AUTTA and performs poorly active-learing strategy under universal shift (Table 1), its balancing idea remains conceptually meaningful. In EMAC, the clustering-comee contrastive module naturally provides an opportunity incorporate such balancing. We therefore introduce BiTTA-inspired weighting
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Table 21: Effect of BiTTA- balancing EMAC. All numbers are AH (56).
Method
(%)
EMAC (w/o BiTTA-style balancing)
52.2
EMAC (with BiTTA-style balancing)
53.1
+0,9
EMAC" (w/o Biyye balancing)
53.1
EMAC (with BiTTA-style balancing)
53.8
+0.7
Table 22: Sensitivity of EMAC to EMA factor a and reward weights (wiils Whew) on DomainNet.
numbers are harmonic mean accuracy Az in %, averaged over all 6 transfer tasks. The default setting (matching Table 1) is highlighted in bold.
Setting
a
(Wold: Wnew)
Varying EMA factor (fixed (1.0, 1.0))
EMA-0.7
0.7
(1.0, 1.0)
51.8
EMA-0.9
0.9
(1.0, 1.0)
52.2
EMA-0.99
0.99
(1.0, 1.0)
52.0
Varying reward weights (fixed a
0.9)
RW-0.5
0.9
(0.5, 1.0)
51.9
RW-10
0.9
(1.0, 1.0)
52.2
RW-2.0
0.9
(1.0, 2.0)
52.1
term balance the contribution of labeled and unlabeled samples the contrastive objective. This modification does not alter the core behavior of EMAC but improves the stability of prototype refinement when sparse annotations are available.
Table 21 reports the AH score averaged across DomainNet benchmarks. The incorporation of the balancing term yields slight but consistent improvements for both EMAC and EMAC" confirming its complementary nature. These results suggest that while BiTTA is not suitable as standalone AUTTA method, its lbeleddunlabeed modest, are consistent across settings, supporting claim in the rebuttal that the benefit is conceptual rather than algorithmic. We will integrate this variant in the released code for reproducibility.
balancing insight can complement EMAC. The improvements, though
A.9 SENSITIVITY TO EMA FACTOR AND REWARD WEIGHTS
As discussed in the rebuttal, EMAC in fact uses fewer tunable hyperparameters than may appear. There is no "merging factor" Amerge in our method (this parameter does not exist in the current paper or code), and the "GMM threshold" 7 is not fixed scalar that needs be tuned. Instead, the boundaries between (Noaa Xmixx Kmew) are determined adaptively by the two means Pola and fnew of the (V)GMM fitted ||=ukoowaa|3 in each batch. Thus, the segmentation fully data-driven and does not introduce an extra global threshold.
In practice, this leaves only two actual hyperparameters:: (i) the EMA factor a used to smooth the reward statistics, and (ii) the reward weights w = (wold, Whew) that balance old-class and new-class rewards. To assess their impact, we conduct sensitivity study on DomainNet by varying a and w around the default setting and reporting the harmonic mean accuracy Ah averaged over all 6 transfer tasks (same protocol as Table 1). Results are summarized in Table 22
Across all tested configurations, the performance of EMAC is highly stable: changing in the range [0.7, 0.99] or reweighting old/new rewards within (0.5, 2.0) leads to fluctuations within zs 0.4% in Ay. We therefore reuse the same default setting (a 0.9, Wold 1.0, Waew 1.0) across all datasets (DemainNet, VisDA-C, ImageNet-C) without per-dataset retuning, suggesting that the tuning effort for new datasets is limited.
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Table 23: Comparison of different label-budget schedules DomainNet (averaged over all 6 transfer tasks). Both strategies use same total label budget: we report the percentage labels spent early/mid/late parts the stream and the final Ar %.
Strategy
Early (0-20%)
Mid (20-60%)
Late (60-100%)
Final Au
Front-loaded (large K early) Steady small-batch (ours)
70%
20%
10%
51.4 52.2
35%
40%
25%
A.10 EFFECT OF BUDGET SCHEDULE
Label-budget schedule and exploration strategy. The reviewer asked whether our label querying is uniform over time or concentrated in early batches, and whether an "early exploration" strategy would be preferable to a steady, drip-like querying scheme.
EMAC does nor use a fixed or manually designed schedule. Instead, it employs an adaptive budget allocation driven by the reward mechanism: batches with higher expected information gain are more likely to be queried. As result, the algorithm naturally tends to request slightly more labels early in adaptation, when the model is least calibrated and gradually reduces the querying rate as the representation becomes more stable. This behavior emerges from the reward dynamics rather than from hand-crafted schedule. To avoid front-loading the entire budget, we also impose top- K constraint per batch, ensuring that annotation remains distributed across the stream and preventing the model from missing difficult later samples. Thus, EMAC maintains both early responsiveness and long-term coverage.
Motivated by the reviewer's suggestion, we further compare two explicit budget schedules on DomainNet (averaged over all transfer tasks): (i) front-loaded strategy that allocates large K in early batches (simulating aggressive early exploration), and (ii) a steady small-bate strategy that keeps a smaller K throughout the we report the proportion of labels consumed in different stages of the stream and the final harmonic mean accuracy Aи- The results are summarized in Table 23
(our default). Both variants
the total label budget;
We observe that the steady schedule yields better final performance than aggressively front-loading the budget, despite using the same total number of annotations. This aligns with our framework: the musstrring-contrastvv loss benefits from receiving small amounts of high-quality labels over time, continuously refining prototypes and mitigating pseudo-labed drift. In contrast, aggressive early querying tends to deplete the budget before encountering later, more informative mixed-region samples, which leads to mildly lower Amr.
A.11 LOCAL EXPECTED RISK REDUCTION BOUND FOR EMAC
In this section provide formal justification for why the proposed two-stage (mixture- + reward-guided) selection combined with the clustering-contrastive update leads to a monotonic decrease in a surrogate risk. The derivation follows the expeetedrrisk-rednctio view commonly used in active learning theory, adapted to the UTTA setting.
Preliminaries. Let ft denote the model time and D, the (unknown) test-time distribution that step. The instantancous risk is
R₂ = E(zw) (x)(())
Since UTTA involves streaming non-IID inputs, dual class domain shift, and a source-free constraint, classical pool-based AL bounds are not directly applicable. We therefore develop local expected- risk-reduction bound.
Let Q be the query distribution produced by two-stage selection. We denote by IG(x:) the information-gal term used in AL theory (e.g., expected model change or posterior entropy reduction). Since this term cannot be evaluated directly in UTTA, the mixture-batt based separation score
sep(#)
|0n u2l post(:))
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Published as a conference paper at ICLR 2026
where (M1 (22) are the component means of the (V)GMM fitted the orthogonally-dccompooo statistic and post(2) is the posterior belonging to the mixed component. This quantity a standard surrogate of information gain in OSFDA/SFUDA literature.
Expected Risk Reduction. A general AL identity (Settles 2012; Golovin & Krause 2010) writes:
[R, - R4+1] = À Q 5,,
where 6, captures error amplification from pseudo-label noise. Using sep(z) as surrogate for IG) yields:
E(R. Re+1] 2 A E.En - 6.-
(1)
Bounding Pseudo-Label Noise. Let Pmux be the fraction of queried samples that lie the mixture region exposed by our two-stage selection. These samples precisely those most prone pseudo- label error. Querying them reduces the magnitude of error amplification. Following noisy self-training analyses:
8.
(1
P Pmix
8mna
(2)
where 6mnix is the worst-case amplification factor.
Because EMAC explicitly prioritizes mixture-region samples, we have:
P query
ma&
Pmix-
over all AL policies
Thus EMAC minimizes the noise term in (2).
Effect of the Contrastive Update. The clustering-contrastive update has a well-known Lipschitz property (Arora et al., ICML 2019; Wang & Liu, PAMI 2021). Updating with labeled sample induces prototype-margin change:
7++1 2 e- sep(#),
where e depends on the smoothness the contrastive objective.
Since wider prototype margins imply strictly smaller surrogate risks for cross-entropy and con- trastive losses, this provides an additional guarantee that gigher-separationn samples reduce risk more effectively.
Final Bound. Combining (1), (2), and (4), we obtain:
R++1 S R, X Ex~0, E [sep(>:) (1 P Pmix
queryy
The two-stage selection maximizes E [s))(()) while minimizing the pseudo-label term via maximizing query Therefore EMAC yields monotonic local decrease in surrogate risk, fully explaining its empirical superiority under identical annotation budgets.
A.12 WRITING PROCESS ENHANCEMENT WITH LARGE MODELS
In compliance with ICLR guidelines, disclose that large models were incorporated into writing process to improve grammar and syntax. The goal was to produce a fluent and well-structured manuscript that adheres academic writing conventions, ultimately enhancing the readability and impact our work.
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Published as a conference paper at ICLR 2026
Symbol
Meaning
Ds
Source domain
DUT
Target test domain
f(; 0)
Pre-trained model
DUT
Annotations budget
Wels
Classfier
konwn
Source-known vecor base
unkkown
Source-unknown vecor base
A batch of features
konwn
features projected onto Source-known vecor base
Zun Zunkonwn
features projected onto Source-unknown vecor base
p(-)
Probability Density Function density function
Expectations of old classes distribution
Pold
Expectations of new classes distribution
Unew
Kold
The regions of old classes sample
Nnew
The regions of new classes sample The mixed regions of old and new classes Max-Min Entropy (MME) objective function
Xmix
LMME
Told
Impact of annotated samples on retaining old class
I nnew
Impact of annotated samples retaining new class
Rold
Reward value for annotated samples of old class
Rnew
Reward value for annotated samples of new class Reward factor
EMA factor
Fp
high-confidence pseudo-label features set
Fa
annotated features set
Pe
class centroid class centroid set Pseudo labels are characteristic of the old class
P
Fold
Fnew
Pseudo labels are characteristic of the new class
Rold
Augmentation are characteristic of the old class
Rnew
Augmentation are characteristic of the new class
Le
Clustering-contraste loss
Ao
Old classes Accuracy of target domain
Ax
New classes Accuracy of targel domain
Aμ
Harmonic mean of accuracies for old and new classes
Table Summary Symbols.
26


Reviews:
[{"review_id": "rykUXrTzvF", "reviewer": "Reviewer_F4qu", "summary": "This paper introduces Active Universal Test-Time Adaptation (AUTTA), a novel framework that integrates active learning into Universal Test-Time Adaptation (UTTA) to handle both class shift and domain shift simultaneously in streaming test data. The authors argue that existing UTTA methods struggle under dual shifts due to unreliable pseudo-labeling, and that traditional active learning strategies fail to select the most informative samples in such open-set scenarios. To address this, they propose EMAC (Exposing Mixture and Annotating Confusion). Extensive experiments on DomainNet and VisDA-C demonstrate that EMAC achieves state-of-the-art performance under dual-shift conditions, outperforming existing TTA, UTTA, and ATTA methods.", "questions": "See weaknesses above.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper clearly defines and motivates the AUTTA setting, which combines the challenges of Universal Domain Adaptation (open-class shift) with the constraints of Test-Time Adaptation (source-free, streaming data) and the strategy of Active Learning (limited human annotation).\n2. The paper provides comprehensive experiments across multiple datasets (DomainNet, VisDA-C) and shift scenarios (corruption, style transfer). The results consistently show EMAC outperforming a wide range of strong baselines.", "weaknesses": "1. The core \"Exposing Mixture\" step relies on the target data distribution being bimodal in the decomposed feature space. While the authors address this in the appendix (A.2), showing robustness with VBGMM and PCA, this remains a potential failure point if the underlying data does not exhibit this property (e.g., highly overlapping or multi-modal new classes).\n2. The overall framework involves several components (SVD, GMM/VBGMM, reward computation with EMA, clustering-contrastive loss) and associated hyperparameters (GMM threshold τ, merging factor λ_merge, EMA factor α, reward weights ω). While the paper provides values, the sensitivity and tuning effort for new datasets could be a concern.\n3. Although the paper compares against several active learning methods (Table 3), the field of active learning for open-set or domain adaptation scenarios is rich. A more detailed discussion on how EMAC specifically advances beyond the limitations of these methods in the test-time setting would be beneficial.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "5DDuNMyTa7", "reviewer": "Reviewer_iQMG", "summary": "This paper proposes Active Universal Test-Time Adaptation (AUTTA) scenario and an approach named EMAC under joint class and domain shifts. The key idea is to actively select a small number of test data to label in the test data stream. EMAC first exposes a mixed region by decomposing the classifier to obtain “known/unknown” subspaces to fit a bimodal GMM distribution to split them.  Further, it uses a Max–Min Entropy reward that prefers old-class samples reducing entropy and new-class samples improving separability to select sample to label. Finally, the model is trained with a clustering-based contrastive objective that balances limited true labels with a lot of pseudo-labels. Experiments on DomainNet and VisDA-C report improved open-set metrics compared to prior UTTA/TTA methods.", "questions": "1. **Budget schedule.** Is label querying uniform over time or concentrated early? Would early exploration work better than steady drip? \n2. **When does the GMM assumption fail?** Can the authors provide diagnostics before selection that decide whether to trust the bimodal split? What happens when the batch is dominated by new classes, or when domain shift inflates norms uniformly?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. **Clear problem framing.** The paper tackles UTTA with dual shifts and argues why naive uncertainty sampling can be biased and how human-in-the-loop labeling can help.\n2. **Lightweight mechanics.** The SVD decoupling and per-batch GMM are simple to implement and compatible with standard classifier heads.\n3. **Attention to real practicalities.** Small-batch instability are considered and adds a sliding window with EMA smoothing to stabilize GMM fitting. EMAC also behaves well under noisy annotations.", "weaknesses": "1. **Incremental novelty; inherits much of OPTTT’s core.** At EMAC core, the paper targets the open-set TTA problem and largely inherits the OPTTT recipe, which propose the scenario and evaluation. It also assumes a bimodal GMM distribution of known and unknown samples and leverage pseudo-label self-training. The main new pieces are an active selection heuristic and a prototype/contrastive auxiliary loss. Useful in practice, but conceptually incremental rather than a substantial shift in paradigm.\n2. **Active learning feels bolted on for extra points ** The contribution narrative hinges on the AL module, yet its design is relatively standard (uncertainty/entropy flavored) and evaluated against modest baselines. There’s limited exploration of stronger, mixture-aware AL strategies. As a result, the AL part reads as an add-on to lift numbers rather than a principled advance.\n3. **Fairness of comparisons (label budget)** Apart from ATTA, most baselines are not evaluated under the same human-in-the-loop budget in Table 1,2,3. Comparisons that pit AUTTA (with labels) against UTTA/TTA methods (without labels) are not budget-fair. A more informative yardstick would be TTA/UTTA methods + the same label budget augmented with a generic AL wrapper.\n4. **Human-in-the-loop latency & hidden costs.** The method’s practicality hinges on online annotation during streaming. This induces stop-and-go adaptation. Even with small budgets, fixed per-event overheads can make real-time TTA infeasible.\n4. **Scope vs. claims.** Although pitched as “universal” TTA, the empirical scope is closer to open-world classification under a couple of standard datasets. Stronger evidence across more diverse distribution shifts (such as corruption datasets like ImageNet-C) would better substantiate the universality claim.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "C8OPmpKpG2", "reviewer": "Reviewer_4PKJ", "summary": "This paper proposes AUTTA and instantiates it with EMAC to handle simultaneous class shift and domain shift at test time by querying a small number of human annotations and combining them with pseudo-labels. The key ideas include decomposing features to expose the dual-shift “mixed” region through a GMM over known/unknown spaces; actively picking the most beneficial samples with an entropy selector; and balancing real labels and pseudo-labels with a clustering-based contrastive objective so that limited annotations don’t get drowned out. On DomainNet and VisDA-C, EMAC improves over TTA/UTTA/ATTA baselines and stays strong under dual-shift, non-i.i.d., and continual settings.", "questions": "NA", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. Moving from UTTA/ATTA to Active UTTA (AUTTA) is a natural and relevant step.\n2. The insight that dual-shift mixed regions are where pseudo-labels fail the most is valuable, showing good motivation.\n3. The method design is efficient.\n4. Results on DomainNet and VisDA-C are comprehensive and strong.", "weaknesses": "1. The core of this paper relies on detecting a bimodal distribution of “unknown energy” after orthogonal decomposition. This can easily break when batches are small, class mixture is skewed, or shifts are not cleanly bimodal.\n2. Orthogonal decomposition on classifier weights is not justified well.\n3. DomainNet + VisDA-C are standard, but both are still vision benchmarks with relatively clean image-level labels.\n4. They mainly report AO/AN/AH; but AUTTA’s value is in not wasting labels.\n5. They reference AL/IG connections and ATTA theory, but don’t give a full bound specialized to their two-stage selection + contrastive update.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WIosd0B3NM", "reviewer": "Reviewer_uM52", "summary": "The paper proposes a new problem setting of Active Universal TTA (AUTTA), providing a few active samples in open-world TTA when both domain and class shifts occur. They propose EMAC (Exposing Mixture and Annotating Confusion) as the solution.. EMAC first exposes mixed “dual-shift” regions by orthogonally decoupling features and fitting a (bi)modal GMM, then annotates a small set of target samples via a reward-guided entropy criterion, and finally applies a clustering-aware contrastive objective to better use scarce labels and pseudo-labels. Evaluations on DomainNet/VisDA (and Office-Home) report gains in UTTA metrics versus TTA/UTTA/ATTA baselines under an AUTTA (human-in-the-loop) protocol.", "questions": "1. Please refer to my weakness above.\n\n1. Equation (4) - I think the old and new pdf should be averaged (not summed) to consist a valid pdf?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Timely problem and setup of moving beyond pure UTTA to an active variant that targets the hardest mixed regions is well-motivated and practically relevant. \n\n1. The EMAC method is intuitive for AUTTA setup.\n\n1. Consistent improvements in AH over strong TTA/UTTA/ATTA baselines on DomainNet/VisDA (and Office-Home).", "weaknesses": "1. Related works on active TTA are missing: EATTA [1], BiTTA [2]. I would appreciate if these could be included in the evaluation. Also, balancing between labeled and unlabeled samples can have some references/insights from BiTTA.\n\n1. The paper positions EMAC as an AUTTA method, yet much of the presentation borrows UTTA terminology/metrics (AO/AN/AH). Please explicitly specify, in the main text, the evaluation protocol for each table and method: (i) train/test dataset class separation setting, (ii) the labeling budget, (iii) the querying schedule, and (iv) how UTTA baselines are run/comparable under an AUTTA setting (including whether active samples are allowed). I would say the fair comparison should allow same number of active samples for all methods.\n\n\n\n\n[1] Wang, Guowei, and Changxing Ding. \"Effortless active labeling for long-term test-time adaptation.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\n[2] Lee, Taeckyung, et al. \"Test-Time Adaptation with Binary Feedback.\" Forty-second International Conference on Machine Learning.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)