PAS: Estimating the target accuracy before domain adaptation
Authors: Raphaella Diniz
Jackson de Faria
Martin Ester
Venue: ICLR 2026 Poster (2026)
Keywords: Transferability estimation
Domain adaptation
Primary area: transfer learning, meta learning, and lifelong learning
TL;DR: We propose the PAS score designed to estimate the transferability of a source domain set and a pre-trained feature extractor to a target classification task before actually performing domain adaptation.

Abstract: The goal of domain adaptation is to make predictions for unlabeled samples from a target domain with the help of labeled samples from a different but related source domain. The performance of domain adaptation methods is highly influenced by the choice of source domain and pre-trained feature extractor. However, the selection of source data and pre-trained model is not trivial due to the absence of a labeled validation set for the target domain and the large number of available pre-trained models. In this work, we propose PAS, a novel score designed to estimate the transferability of a source domain set and a pre-trained feature extractor to a target classification task before actually performing domain adaptation. PAS leverages the generalization power of pre-trained models and assesses source-target compatibility based on the pre-trained feature embeddings. We integrate PAS into a framework that indicates the most relevant pre-trained model and source domain among multiple candidates, thus improving target accuracy while reducing the computational overhead.
Extensive experiments on image classification benchmarks demonstrate that PAS correlates strongly with actual target accuracy and consistently guides the selection of the best-performing pre-trained model and source domain for adaptation.

Published as a conference paper at ICLR 2026
PAS: ESTIMATING THE TARGET ACCURACY BEFORE DOMAIN ADAPTATION
Raphaella Diniz, Jackson de Faria Júnior & Martin Ester School Computing Science Simon Fraser University Canada
(raphaella.dinis jackson.de.faria.juiii ester]
ABSTRACT
The goal of domain adaptation is make predictions for unlabeled samples from a target domain with the help labeled samples from different but related source domain. The performance of domain adaptation methods is highly influenced by the choice of source domain and pre-trained feature extractor. However, the selection of source data and pre-trained model is not trivial due to the absence a labeled validation set for the target domain and the large number of avail- able pre-trained models. In this work, we propose Potential Adaptability Score (PAS), novel score designed to estimate the transferability of a source domain set and pre-trained feature extractor to target classification task before actually performing domain adaptation. PAS leverages the generalization power of pre- trained models and assesses source-target target compatibility based on the pre-trained feature embeddings. We integrate PAS into framework that indicates the most relevant pre-trained model and source domain among multiple candidates, thus improving target accuracy while reducing the computational overhead. Extensive experiments on image classification benchmarks demonstrate that PAS correlates strongly with actual target accuracy and consistently guides the selection of the best-performing pre-trate pre-trained model and source domain for adaptation.
INTRODUCTION
In many real applications, data is collected from diverse domains, e.g., data obtained from dif- ferent equipment, collecting procedures, geo- graphic locations, or periods in time. Such differences may lead to a distribution shift be- tween the domains that must be assessed. Un- supervised domain adaptation is a paradigm where only unlabeled data is available for the domain of interest, the target domain. However, labeled data obtained from a related source domain.
Tanget dumoin
Cunditase
Tonge dunsin accumnay ater domais edaptation
pee trined models
FAS value
Model
0,97
Modic 2
20.
0
0.76
2013
Candidune
domeins
0.32
Domuin A
0.01
Domain
One factor that affects the success of domain adaptation methods is the choice of the source domain data. Domain adaptation methods often rely on many assumptions about the relation- ship between source and target domains, like the existence of invariant discriminative fea- tures, the similarity of the label distribution, or the invariance of the task. Unfortunately, as the labels for the target samples are not available, such assumptions may not be verified in real applications for selecting the most appropriate source data. Violating the data assumptions and
Highi coeriitio
The Potential Adaptability Score (PA.S) estimates the performance of adapting unlabeled target domain given a pre-trained fea- ture extractor and labeled source domain. helps in the selection of the best pre-trained model and best source domain among many candidates and is highly correlated with the final target accu- racy after domain adaptation.
Figure 1:

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considering an irrelevant distant source domain may introduce noise and conflicting patterns dur- ing the domain adaptation process. In the worst-case scenario, selecting an undesirable source do- main may hurt the target domain performance, scenario known negative transfer Zhang et al. (202)). If many source domains are available, it is reasonable to assume that not all of them may contribute equally the target adaptation. Wisely selecting the source domain that may improve the performance on the target data while avoiding negative transfer is an essential requirement in many real-world applications.
Another key factor that influences the domain adaptation performance is the choice of the pre-trained model. Pre-training on large-scale data allows the models to learn generic features and patterns that are often transferable across domains and tasks, making them valuable for domain adaptation. Recently, practitioners can choose from a vast number of publicly available pre-trained models, spanning diverse architectures and training paradigms. Fach pre-trained model may have its own inductive bias and may capture distinct patterns in the data that may be more or less useful when transferring knowledge between domains.
Despite the importance of selecting a suitable source data and a pre-trained model for the suc- cess of domain adaptation, it is still an underex- plored topic. Current methods of transferabil- ity estimation aim to select the best pre-trained model for transfer learning. However, these methods are not applicable to the domain adap- tation scenario since they require target labels
X
x
x
x
Bao et al. (2019); Nguyen et al. (2020); You et al. (2021). One could employ such methods for selecting the best pre-training model using only the labeled source data and ignoring the unlabeled target data. Nevertheless, consider- ing the target data is essential, as transferring to an easy target domain should lead to different results than transferring to a harder one.
83 Target sample
Source sample
Class centroid
Class A
Class B
Class C
Unknown class
Distance to the class centroid
Another approach for the problem would be performing domain adaptation for each com- bination of available source domains and pre- trained models, and applying some model se- lection strategy Friesson et al. (2023); You et al.) 2019); Sun et al.J (2021). However, this ap- proach very time-consuming since it needs to run a domain adaptation algorithm for each combination.
d, Distance to the nearest class ventroid. Preferably small.
d,
Distance to the second nearest class centroid. Preterably large.
Figure 2: Source and target samples in the embed- ding space of pre-trained model. (top left) Ide- ally, target sample from given class should be more similar, and hence closer in the embedding space of the pre-trained model, to a source sample from the same class. (top right) If new discrim- inative features need to be learned, the chances of overfitting on the source domain during adap- tation increase. (bottom) Illustration of the dis- tances from a target sample to all source class cen- troids. Our PAS score considers the relationship between distances dj and d2, which correspond to the shortest and second shortest distances, respec- tively.
A third approach would be to measure the dis- tance between source and target feature dis- tributions in the embedding space of the pre- trained model. This approach is also chal- lenging the popular metrics for the distance between two feature distributions are symmet- ric, e.g., Maximum Mean Diserepancy (MMD) Gretton et al. (2006). Wasserstein distance Val- lender (1974). and CORAL Sun & Saenko (2016). However, a metric suitable for our sce- nario should be asymmetric because transfer- ring from an easy to a hard domain is more challenging than transferring from the harder domain to the easier one.
In this work, we examine the interplay between the three key components in the domain adaptation setting for classification: (1) target data, (2) source data, and (3) the pre-trained model. We propose the Potential Adaptability Score (PAS). a simple but effective novel measure to quantify the po- tential success of using a pre-trained model to transfer knowledge from source domain to the target
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domain. Our experiments show how the PAS score is highly correlated to the final target accuracy
after adaptation.
To the best of our knowledge, this is the first proposal for transferability estimation for the domain adaptation setting. We demonstrate how PAS can help to select the most relevant source domain and/or pre-trained model among set of candidates, indicating the options that are most likely to lead to the best accuracy on the unlabeled target data (See an overview in figure Our framework selects the most suitable options before actually performing domain adaptation, demanding fewer computational resources and reducing the training time.
large-scale dataset, such as the popular ImageNet- Ik Deng et al. (2009). Specifically for domain adaptation, initializing with good pre-trained model appears to be a fundamental step in achieving a good transferability between do- mains Peng et al.] (2018); Tang & Jia (202)); Kim et al. 2 Li et al. (2023); Teterwak et al. (2023). We assume that good pre-trained model can extract general discriminative features that are robust across all domains. If this assumption is true, samples from the same class are expected to be closer together in the embedding space generated by the pre-trained model, compared to sam- ples from different classes, even in the presence of feature distribution shift. This ideal scenario is illustrated in the top left of the figure Otherwise, as shown in the example on the top right of the figure, the model should Iearn new discriminative features during the adaptation from the limited la- beled source data to enable the classification task, increasing the chances of overfitting to the source domain. Our PAS score is inspired by the Silhouette score, used for assessing the consistency of data clusters Rouuseeuw (1987). We modify the original Silhouette score to measure the similarity of the unlabeled target samples some of the known source class clusters defined in the pre-trained embedding space.
PAS leverages the generalization power of models pre-trained
We summarize our contributions follows:
We propose PAS, a simple novel measure to quantify the potential contribution a pre- trained model and labeled source domain in the adaptation to unlabeled target domain before performing domain adaptation.
We propose a framework select the most relevant pre-trained model or source domain from a collection of potential candidates for performing domain adaptation.
We empirically validate our framework using different domain adaptation methods and image classification benchmarks, and show how our score has high correlation with the target accuracy.
RELATED WORK
Unsupervised domain adaptation. The goal of unsupervised domain adaptation (UDA) is to transfer the knowledge learned from a labeled domain to different unlabeled target domain. Usu- ally, this goal is achieved by learning a latent representation that is invariant across domains. Several works minimize the distribution discrepancy on the representation using statically defined distance metrics such as Maximum Mean Discrepancy (MMD) (e.g.. DAN Long al. (2015). DDC TTeng et al. (2014), JAN et al. (017)), covariance (e.g., OCORAL & Saenko (2016)), ), or Wasser- stein distance (e.g.. DOe Damodaran al. (2018)). The popularization of generative models inspired the proposal of methods that adopt adversarial learning to align data across different do- mains. DANN Ganin al. (2016), CDAN Long et al. 20188. and ADDA Tzeng al. (2017)
examples of widely adopted UDA methods that have shown promising results. Self-training is an- other promising paradigm that exploits the pecudo-labele predicted for the target domain to enhance the model. CST Liu et al. (2021). CRST et al. (2019), FixMatch Sohn et al. (2020) and MCC jt al. (2020) are examples methods that explore pseudo-labeling. Most recently, with the dissemi- nation of transformers and foundation models,
works explore the cross-attention mechanism to propose transformer-bast based domain adaptation methods, such as PMTrans Zhu et al. (2023) and DoT
Ren al. 2024). See Liu et al. (2022); Deng & Jia (023) Alijani al. (2044) for comprehensive survey on domain adaptation methods.
Pre-training and domain adaptation Recent works suggest that the choice of the pre-trained feature extractor can significantly improve the result of domain adaptation methods. Teterwak et al. 2023) show that simply adopting model with better weight initialization help the robustness
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of model to -isti--iiitiibuttio samples. Similarly, Kim al. (2022) empirically show that SOTA pre-training outperforms SOTA domain adaptation methods even without access to a target domain. With modern pre- pre-trained backbone, older domain adaptation methods perform better than SOTA methods, but no method is consistently better in all benchmarks, and negative transfer can occur. Li et al. (2023) empirically show how, in some cases, the performance of the pre-trained model in an unseen target domain is already decent. However, no single pre-trained model performs well in all target datasets. [ang & Jial (2023) study the effects of pre-training on the domain adaptation between synthetic and real images. Without pre wre-training, none of the methods considered in the study outperformed the baseline trained only on the labeled source data. Other studies have also proposed new datasets and pre-training techniques that achieve competitive performance in the target domain [Shen et al. (2022); : Luo et al. (2024). We leverage the potential relationship between pre- training and domain adaptation success to estimate transferability between domains.
Transferability estimation In the past years, many works have proposed scorees for quantita- tively estimating the transferability of pre-trained model to target task. One of the primary practical applications of such estimation is selecting the best pre-trained model for fine-tuning on the target data. H-score Bao et al. (2019), NCE Tran et al. (2019), LEEP Nguyen et al. (2020) and LogME| You et al.] (2021) are widely adopted transferability estimation scores. More closely related our proposal, some works propose scores for transferability estimation by examining the separa- bility of classes in the embedding space encoded by the pre-trained model. Paáddy et al. (022) apply Meiseles the Bhattacharyya coefficient to quantify the target class separability. Miiielesy & Rokach (2020) employ the Silhouette score to assess the transferability of time series data. The current methods on transferability estimation focus on the transfer learning problem, where pre-trained model is adapted to target task with few labeled samples. Unfortunately. these methods can not be applied to the domain adaptation problem, where the target labels are not available.
3 METHOD
3.1 DEFINITIONS
Unsupervised domain adaptation aims to transfer knowledge from a labeled source domain to an unlabeled target domain in the presence of distribution shift. Let X S Rd define the input space and |
y {I...., C} the label space. The labeled source dataset is denoted by DS {(=; ) and the unlabeled target dataset is denoted by D = {xx { 2,2 with ",, E X and uf E y. S denotes the set of source samples from class c. The source and target feature distributions are sampled from different but related distributions, (2())) and Pr (XY), respectively, being Ps * Pr- This scenario is also known as covariate shift. The goal is to learn a hypothesis : X - y that performs well on the target domain.
Fiml
Let be the parameters a feature extractor fo : X + Z pre-trained on a large-scale dataset. z5 = fo(x;) and z fo(x?) denote, respectively, the embedding of a source and target sample in the embedding space defined by fo.
3.2 ASSUMPTIONS
We assume that good pre-trained model So is able extract wide range of patterns and high-level concepts from an input, including discriminative features that are invariant across different domains. We expect that samples from the same class are more similar, having many concepts in common. As a result, two samples from the same class should be closer together in the embedding space Z, no matter the domain. On the other hand, samples from different classes should have very few concepts in common, resulting in a dissimilar embedding representation. Due to the distribution shift between the source and target domains, samples from the same domain are expected to have more concepts in common and, therefore, have more similar representations than samples from different domains. Such assumptions lead to a scenario similar to the one represented in the top left of figure 2 The embeddings of samples from the same domain and class form a well-defined cluster in the space encoded by fo. Also, the clusters of samples from the same class, but different domains, are closer together and, ideally, both are distant from all the other clusters.
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To summarize, assume that good pre-trained model can extract invariant discriminative features, 2) samples from the same class are close in the embedding space, even if they are from different domains, and 3) samples from different classes are distant in the embedding space. Similar assumptions are proposed by [Shen et al. 2022) when studying the generalization of embeddings to out-of-distribution samples.
3.3 THE POTENTIAL ADAPTABILITY SCORE
We introduce the Potential Adaptability Score (PAS) measure of the distance from laheled source dataset to an unlabeled target dataset in the embedding space encoded by pre-trained feature extractor. The PAS score is based on the expectation that each target sample is close possible to source samples from single class and significantly distant from source samples from all other classes in the embedding space defined by a pre- rrettrined model fo. This means that target sample is very similar to source samples from one class and has only few concepts in common with source samples from all other classes, as illustrated in figure 2 The higher the PAS value, the stronger the evidence that the pre-trained model can identify invariant discriminative features between the domains and, consequently, the higher the chances that the pre-trained feature extractor fo has a good transferability from the source to the target samples.
The samples are normalized to unit length, and the distance between samples is calculated using the cosine distance. We assume that the samples from the class Y are clustered together. We follow Dhillon & Modha (2001) and compute the centroid of each source class cluster c so they represent the vector that, on average, has the highest cosine similarity all the samples in the cluster.
Me Enfesg So(x;)) EEgees fo(x)))l
(1)
For each target sample x!!', we calculate its cosine distance to the centroid of each source cluster:
dist(fo (4) = (fa(x.) pc).
(2)
Let D; = (oist(fo( ), pi),..., dist( KC)} be the set of distances of the j-th target sample to all the source clusters and sort D2) the sorted version of the set in ascending order. We define
du = [1] and do: = sort( [2) as the shortest and the second shortest of the distances, respectively, illustrated the bottom of figure
Finally, the PAS score is defined by
D
PAS(O,D5,p0) DT. 1
Σ
d2 d1s
(3)
DD'
d2i
Given one or more candidate source domains and a set of pre-trained models, the PAS score can help select the options that are more likely to lead the best accuracy on the target samples. The selection is done by computing the PAS score for each trio of target domain, source domain, and pre-trained model. The combination with the highest PAS value is chosen. The selection is done before any domain adaptation training. A single-source domain adaptation method can then be trained with the selected source domain and pre-trained feature extractor.
Our PAS score is inspired by the Silhouette score, used for assessing the consistency of data clusters Rousseeuw (1987). The Silhouette is a supervised score calculated by (b - a) a//mar (a, (a,b), where is the mean intra-cluster distance and b is the mean nearest-cluster distance for each sample. It ranges from - to with higher values indicating strong intra-class cohesion and clear inter-class separation. Note that the Silhouette score is fully supervised and designed for IID samples and its original form is not suitable for the domain adaptation problem. Our PAS score is an adaptation to accommodate unlabeled target samples and domain shift. We consider the closest source cluster as the true class for each target sample. This assumption makes a always smaller than b, and restricts score the interval [0, 1). The PAS score is close one if the samples from the target domain
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100
90
80
Accurocy
70
60
50
Office Home Office 31 ImageCLEF DomainNet
40
0.079
0.136
0.193
0.249
0.306
0.363
0.420
0.476
0.533
0.590
PAS metric
Figure 3: The correlation between the PAS score value and the target accuracy after the domain adaptation. Each box summarizes the target accuracy of different domain adaptation methods for a given source-target pair and a pre- pre-trained feature extractor. Higher values for the PAS score are strongly correlated with higher target accuracy.
are similar to the centroid of the source class cluster. However, due to the mismatch between the domains, the target samples exhibit a shift in the feature distribution, making a larger than in the IID scenario. As result, the values for our score are typically smaller. Alternative design choices are discussed and evaluated in section
EXPERIMENTS
Datasets. We evaluate PAS on four of the most popular benchmarks for domain adapta- tion: Office-Home Venkateswara et al. (2017).
ce-HHome
Office-31 (W-+A
Office-31 Saenko et al. (2010). ImageCLEF]
and DomainNet Peng et al. (011)). The bench- marks' statistics are listed in the table
205
0.08
0.20
0:12
ode
0:00
012 0.14
0.1
0.1
Domain adaptation methods Many domain adaptation methods have been proposed in the literature, but none have consistently outper- formed the others in all benchmarks. For this reason, we obtained the published target accu- racies of large variety of -fate-of-thhe-at meth- ods. We consider methods based on different paradigms and trained using diverse pre-trained feature extractors. The accuracy values are ob- tained from the popular open-source Tllib li- brary for transfer learning Jiang et al. (2022); Junguang Jiang 2020). Wang et al. (2023) and from the original papers.
TAS mebric
RAS meris
de wew.sneeeeeeeee DANN MDC
mecetto
bast, panchie, 224
bese www.:/ttttttt.eeee 224,
Figure 4: The PAS value and target accuracy for the DANN and MCC methods using different pre- trained feature extractors. The PAS score can help to select the feature extractor that leads to higher aceuracy. (left) A-C adaptation in the Office- Home benchmark. (right) W-A adaptation in the Office-31 benchmark.
Baselines To the best of our knowledge, PAS is the first asymmetric score proposed for trans- ferability estimation for domain adaptation. We, therefore, compare PAS with the symetric met- rics Maximum Mean Discrepancy (MMD) Gretton et al. (2012) and A-distance Peng et al. (019)). The MMD distance is computed using a Gaussian kernel. Due to the quadratic nature of MMD. we restrict its computation to maximum of 10,000 randomly selected samples per domain for the DomainNet benchmark. The A-distance is computed using C-Support Vector Classifica- tion. We also report the results for an oracle baseline. The oracle is similar to PAS, defined as da. d (dr. The d, distance is the cosine distance to the centroid of the true class of the target sample (not known in real scenarios), and dy is the distance the closest cluster's
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Table I: Average target accuracy of domain adaptation methods and transferability scores for dif- ferent image classification benchmarks. The highest values are highlighted. Our PAS has a high correlation with the target accuracy and, for each target domain, attributes the highest value for the source domain that leads to the highest target accuracy in most scenarios. Oracle baseline that considers the target labels.
(a) Office-
Targst
Coeriition
Source
Peznson
Spearman
Acc. lavg)
62,0
61.7
72.5
53.5
52.4
58.9
71.0
70.0
$2.1
77.2
70,5
78.7
MMD (neg)
-0.135
-0.113
-8JR2
-0.13
-4.977
125
-0.113
-0.097
-0003
+0.052
-0.125
-0,033
0.77
0.72
Ro
A-dictanos (neg)
-18876
-1.810
-1.383
-1886
-18227
-1.884
-1.10
-1 8.87
-1.434
-1,33
-1814
-1.434
0.76
0.78
PAS (our)
0.107
0.143
0.201
0.128
0.156
0.166
0.182
0. 0.168
(L288
0.217
0.147
0.254
[U81
0.82
Oracle*
0,041
0.037
0.893
0.022
0.015
0,022
0.103
0.100
0.218
0.165
0.095
0.195
0.96
0.93
Acc. lovg)
74.3
71.7
76.8
61.8
58.5
61.2
$1.7
83.2
$6.8
83.3
$2.5
84.1
MMD (neg)
-0.106
-0.058
-8.24
-0. 106
-0.977
-0.100
-0.058
-0.077
-0.126
4.024
-0.102
-0.026
0.56
0.57
Deit Small
A distance (ncg.)
1,355
1.761
-1.36
1,865
1.843
-1.796
1.761
1.843
-1.37
-1.26
1.795
1.37
0.52
0.57
PAS (our)
0.143
0.183
0.25
0.175
0.185
0.204
0.266
0.221
0.348
0.295
0.2
0.301
0.67
0.78
Oracle*
0.085
0.112
0.15
0.018
0.057
0.047
0.194
0.155
0.291
0.245
0.147
0.246
0.90
0.93
Acc. (ovg))
81.5
75.8
$1.1
69.9
65.4
66.0
56.0
86.7
9116
87.4
67.2
H5.5
MMD (ncg.)
0,.99
-0.156
.0005
0.099
-0,091
11.11
11056
0.091
-0123
0.112
.0.11
.0.023
0.55
0.57
DelT Buse
A distance (ocg.)
1.822
1.31
-1372
1.822
19907
-1.255
1.81
1,907
-1502
-13372
1.823
1.502
0.48
0.51
PAS (our)
0.138
0.176
0.343
0.166
0.172
0,194
12245
0.209
0.339
0.287
0.193
0.295
0.65
0.73
Oracle*
0.09
0.112
0.164
0.018
0.009
0.062
0
0.158
0.293
0,245
0.154
0.245
0.85
0.88
Auc. [ovg])
10.1
153
82.2
66.4
83.2
68.2
14.1
84.3
89.0
$7.2
H5.5
MMD (ncg.)
11.16
-0.182
00033
.0.16
-0.115
40.12
11082
0.115
-0034
4.103
-1.122
0.004
0.61
0.49
VIT Small
A distance (ocg.)
1,885
1,883
-1345
1.855
1927
-1.662
1.883
1.977
11.515
-1.348
1.362
1.515
0.53
0.59
PAS (our)
0.172
0.198
0.262
0.182
0.199
0,317
0.251
0.235
0.357
0.294
0.219
0.316
0.68
0.83
Oracle*
0.032
0.147
0212
0.064
0.102
0.115
0.211
0.195
0.321
0,26
0.189
0,285
0.87
0.92
Auc. [ovg.]
B35
R2.7
84.3
752
7222
74,4
13.3
85.6
91.4
901
19.35
19.3
MMD (ncg.)
00,001
-0.859
.0031
-8.101
-0.105
10.11
11.069
00106
-0015
0.1311
.0.11
-0.025
0.59
0.57
WT Base
A distance (ocg.)
1.85
1,845
-1389
-1.85
11952
1.535
1.345
1,922
-1.595
-1339
1,885
1.595
0.47
0.51
PAS (our)
0.284
0.28
0.357
0,262
0.211
0.296
0.36]
0.339
0.463
0.405
0,315
0.417
0.76
0.85
Ouadee
0.315
0,233
0.311
0,173
0.185
0,207
0,317
0.295
0.425
0.37
0.286
0,382
0.88
0.92
Auc. (org]]
18.5
37.7
37.9
78.
77.1
75.2
91.3
01.9
94.3
920
93.0
02.3
MMD (ncg.)
10.81
-0.185
-0039
-0.051
-0.104
-0,097
11.005
-0.144
-0033
0.039
-1097
.0003
0.48
0.45
Swin-Rave
A-dictanc (org)
-1.853
-1.892
-1.00
-1853
-1.95
-1.917
-1.992
-1.95
-1.57
-1.401
-1917
-1.57
0.2
0.37
PAS (our)
0.232
0.251
0,327
0.231
0.344
0.269
0.323
0,318
0.43
0.37
0.294
0.384
0.72
0.72
Ocacle.
0.198
0.214
0.267
0.162
0.177
0.195
0.205
0.252
0.403
0.343
0.27
0.356
0.83
0.61
(b) Office-31
Taga
KD4D legg)
0.385
4.143
0/22
(c) ImageCLEF
2.80
-1.83
0.356
08
0.44
0.00
4.333
-1127
-1123
4.55
0.96
0.54
Taga
Consiitav
11160
01:00
0.73
1.77
6.47
975
10ND sing
0:114
0.823
0.097
4602
6.13
111m
$3.0
108
773
952
Rodie
8.300
1.883
01231
1.311
0.23
0.23
0.200
0A9
Do733a
2.80
2.30
-3.00
2.10
2.80
3.50
03
09
Aac: (ngg
92.7
3.34
3.56
2.12
1
(1213
0164
1.36
04.4
0.2
033
Dc
4000
4.881
4.80
4.11
KIME Jerg
4.1
41115
4.8%
0.%6
094
PAN
8.345
10355
0.25
0.301
0.52
0199
3.36
0.02
841
8.301
1.27
11:00
0.88
177
8A1
654
leng
97.
36.
953
VIT Ro
1.485
17:1
1.5.
4.50
014
BD
MI-Buz
KMM
4choas (agg)
0.12
4003
4.95
4.71
-1953
6/3
Oradi
0331
0432
0.20
0.71
0335
0.56
1.6
8422
0.35
0127
Orucie
0.73
0347
08.
188
915
Swa:Rac
2310
1.51
Duabll
2331
0311
0.36
0.54
1366
(35)
0.6
652
(d) DomainNet
Target
C
R
Correlation with aec.
Stunce
P
R
R
5
C
P
C
R
Peusson Spearmnn
Acc. (RYE)
53.2
10.7
MMD ineg.)
0.113
0.158
0.113
0.158
0.175
0.108
ResNer- 101
A-diciacce (neg
-1.77
-1.65
-1.221
-1,821
(our)
0.148
-0.00
Acc. (ayg.)
45.4
52.4
67.2
MMD ineg.)
0.145
0.128
0.166
0.186
-0088
0.15
DelT Small
didicannce (neg.)
-1.33
-1734
-1,225
11268
(our)
(avg.)
51.6
729
713
MMD ineg.)
0.155
0.125
0.,66
0.71
0.15
0.227
DeiT
dicinnce (neg.)
-1.445
-1,26
-1,82
-1.79
(our)
0.137
0.899
0.119
0.002
Acc. (aVg))
6077
16.2
759
37.2
62.7
76.
MMD (neg.)
0.157
0175
0157
-0.116
-0108
0.175
0.25
didiaance (neg.)
-18114
-.771
-1.33
-1.89
-1,66
11.007
(our)
0.162
0.23
0.176
0.67
-0.003
0.108
7

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Table 2: Statistics of the benchmarks used in the experiments.
Dataset
#Samples
#Classes
Domains
Office-Home
15,588
65
A (Art), C (Clipart), P (Product), R (Real-world)
Office-31
4,110
31
A (Amazon), D (DSLR), W (Webcam)
ImageCLEF
1,800
12
C (Caltech-256), (ImageNet ILSVRC 2012). P (Pascal VOC 2012)
DomainNet 569,010
345
C (Clipart). P (Painting). R (Real), S (Sketch)
Table 3: Correlation with the average target accuracy after adaptation. Showing Pearson correlation / Spearman's 's rank correlation.
Office-Home
Office-31
ImageCLEF
DomainNet
Total
MMD
0.55 / 0.51
0.45 / 0.53
-0.14 -0.08
-0.09 / -0.03
0.37 / 0.37
A-distance
0.32 / 0.17
0.26 0.35
-0.13 -0.07
0.07 0.06
PAS (our)
0.76 / 0.81
0.63 0.78
0.44 0.60
0.53 0.56
0.83 / 0.88
Oracle*
0.89 0.90
0.71 / 0.86
0.78 0.85
0.21 0.21
0.88 / 0.91
centroid that is not the true class. In the ideal case where the closest class centroid the actual class of the sample, the oracle is the same as PAS, otherwise, the oracle value is smaller. The oracle validates the existing relationship between the clusters distance and the target accuracy.
4.1 SELECTION OF THE SOURCE DOMAIN
The results for the four benchmark datasets are presented in T (d). For each source-targe pair in the benchmarks, we group the domain adaptation methods using the same pre- pre-trained feature extractor and report their average target accuracy. followed by the baselines and our PAS score. We highlight the highest values among the different choices of source domains. We also report the correlation (Pearson and Spearman's 's rank correlation) between the average target accuracy and the scores. The detailed results for each individual domain adaptation method are presented in the Supplementary Material A.I
We report in Table the overall correlation for all scenarios each benchmark (all target domains, source domains and pre-trained models). The results show that the PAS score is strongly correlated with target accuracy. We observe an overall Spearman's 's rank correlation 0.88 over all the results.
The most important results are reported in Table 4 where present the correlation for each target domain. This correlation is the most useful for users in real-world scenarios. Given a target domain of interest and many options of source domains and pre-trained models, we show that PAS score has a strong correlation with the final target accuracy. The empirical results indicate that our proposed PAS score is effective in selecting the best source domain among many candidates.
We summarize our results in Figure 3 Each box in the graph represents the target accuracy of dif- ferent domain adaptation methods using the same pre-trained backbone for source-target domains pair. We observe that higher PAS values are consistently related to high accuracy on the target domain. This indicates that PAS may be useful only for selecting the most appropriate source domain, but also to estimate beforehand the success of the domain adaptation.
Table 4: Correlation with the average target accuracy after adaptation for each target domain. Each cell considers the results for a target domain and all available source domains and pre-trained mod- els. Showing Pearson correlation / Spearman's rank correlation.
Cito Hlore
Cffie
brayCLEF
MMD
6-170.34
48877 032
3:56/
0357033
1X357447
41387
PNN
629/8334
HAS BU1
878//675
1U82/ 8.76
0.77 (083
8:1 8xT
DON 70.93
0334025
(06/0.3)
0612512
8

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The results the ImageCLEF benchmark illustrate the scenarios where the PAS score is not ef- fective. This benchmark (especially the domain) contains images with multiple objects. In many cases, the sample is very close to the centroid of one class that is indeed present in the image, but the true label is related to another object in the seene. In these cases, the PAS for the sample is high, showing high similarity with source class, but the final accuracy is low, the sample is classified as the wrong class. We show examples in the supplementary material A.2
4.2 THE SELECTION OF THE PRE-TRAINED FEATURE EXTRACTOR
The results in the literature presented in table com- pare methods with different backbones and demonstrate that PAS can be applied to select the most suitable pre- trained feature extractor. However, they do not con- sider the impact of different pre-trained feature extrac- tors over the same domain adaptation method. For an- alyzing the robustness of PAS over different choices of pre-trained methods, we keep the domain adaptation method fixed and vary the pre-trained backbone. We se- lect two of the most challenging domain adaptation sce- narios: the A CC adaptation in the Office-Hoome bench- mark and W-A adaptation in the Office-31 benchmark. We show results for two popular domain adaptation meth- ods: DANN Ganin et al. (2016) and MCC al. (2020).
0.700
0.155
6156
6125
0.100
02
Figure 5: The PAS value varying with the numher of samples for the Office- Home. The PAS values are quite robust to varying numbers of samples. Most importantly, the relative order of PAS values for different source domains re- mains unchanged.
We train each method following the code provided by the
Tilib library Tiang et al. (202); Junguang Jiang (2020)
with the default hyperparameters. The results are shown in figure 4 Higher PAS values are attributed to pre- trained models that lead to higher target accuracy before performing domain adaptation, indicating that our score may be applied for the selection of the pre-trained model.
4.3
THE IMPACT OF THE SAMPLE SIZE
The time complexity of the PAS computation linear in the number of samples. This can be limiting for quick evaluation of larger datasets and scenarios with many candidate source domains.
To optimize the computation time, we show that our score can be calculated using only a subset of the samples. We randomly select subset of the samples of both source and target domains. The results are presented the figure The PAS values are quite robust to varying numbers of samples. Most importantly, the relative order of PAS values for different source domains remains unchanged.
4.4 DESIGN CHOICES
The PAS score considers the cosine dis- tance between each target sample and the source class centroids. We experimentally evaluated alternative design choices and compare the correlation between the score and the target accuracy. The results are shown in table 5 and demonstrate how the overall correlation between the target ac- curacy and PAS, as proposed, is higher.
Office Home
Office 31
IrngeCCLE
DomannNer
Tocal
PAS
0.76
0.58
Euclidken distana:
0.68
Average sosine distnce
0.52
0.49
Table 5: Pearson correlation between the target accu- racy and the PAS score, which considers the cosine dis- tance to the cluster centroid, and modifications using the Euclidean distance the centroid and the average cosine distance to the source cluster samples. The max- imum correlation value for each benchmark is high- lighted. The design choices of PAS lead to the higher overall correlation between the score and the target ac- curacy.
We change PAS to consider the Euclidean distance instead of the cosine distance. In the domain adaptation setting, the co- using
sine distance has advantages the Euclidean distance in the original la- tent space, it ignores the magnitude of

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Published as a conference paper at ICLR 2026
representations (e.g., difference in the illumination in images that reflects the intensity of the features detected by the model) and focuses only on the differences the angles (the difference between classes). Also, the cosine distance is less affected by the high-dimensionality of the data (the phenomenon known as curse of dimensionaliry (aalllmaaniiyyBlllmaaa 1966)).
We also modify PAS to the average pairwise distance the source samples instead of the dis- tance to the source cluster centroid. The pairwise distance is good summarization the closeness of the target sample the source samples of the class. On the other hand, the distance the centroid measures how well the target sample is aligned to the dimensions of greatest alignment between the samples in the cluster, as the centroid formulation 1//D$1 - x; makes samples pointing in similar directions add up in that direction.
CONCLUSION AND FUTURE WORK
We present Potential Adaptability Score (PAS), new score to select, among many candidates, the source domain or pre-trained model that are likely to lead to the best target accuracy when used for unsupervised domain adaptation. We evaluate our domain adaptation and show that it has high correlation with the target accuracy and selects the best source domain in most cases. We also show that PAS can be computed more efficiently with fewer samples.
four of the most popular benchmarks for
We suggest two improvements for future work. Although our score could be applied to any classifi- cation task, we focus on vision problems, specifically the image classification task, which is the most common task in the domain adaptation literature. Showing its efficacy on other modalities and tasks demands the availability of diverse set of benchmarks and specialized domain adaptation methods. Also, we focus on the single-source domain adaptation problem, where only a single source do- main is considered during the training. Future works may extend our work to select multiple source domains, in the setting known as multi-source domain adaptation.
ETHICS STATEMENT
Although our work does not directly address issues of social harm, we acknowledge that our PAS score is not immune bias and fairness concerns. If biased model achieves higher accuracy on the target data, our framework is likely to select it as the best pre-trained model for domain adaptation. In our experiments, mitigate such risks by focusing on pre-trained models and benchmarks that are widely adopted within the research community.
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A
SUPPLEMENTARY MATERIAL
A.1
RESULTS
The tables 6 and Jshow the extended results of table The accuracy for each method is listed, well its accuracy correlation with the scores.
A.2 EXAMPLES OF SAMPLES IN THE ImageCLEF BENCHMARK
We present some examples of when the PAS score fails to predict the target accuracy. The figure shows examples of misclassified images from the P (Pascal VOC 2012) domain of the ImageCLEF benchmark. Many images contain more than one object. The sample may be very similar to class present in the image. However, the true class refers to another object also contained in the image. In such cases, the PAS value is high, but the accuracy is low.
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Table 6: Target accuracy of domain adaptation methods and transferability for the Office- Home dataset. The highest values are highlighted. Oracle baseline that considers the target labels.
Targei
Correlation with PAS
Peussn
Spearman
DAN
37.7
54.9
66.2
45.6
40.0
49.1
67.1
63.3
77.5
15.3
66.3
14.3
01.74
0131
DANK
02.6
74.0
77.9
0.35
ADDA
76.5
CDAN
62.0
62.4
75.5
55.2
54.3
6L,0
72.4
09-7
83.
71.6
70.9
SU.5
0.85
0.33
MCD
63.7
61.5
74.5
$1.7
$2.8
58.4
72.2
69.5
$1.8
78.2
70.8
75.0
0.78
0.83
50
BSP
61.0
60.9
73.4
54.7
55.2
60.3
67.7
69.4
$1.2
76.2
70.9
80.2
0.85
0.50
77.0
83.0
0.76
FixMarch
65.3
67.2
14.9
56.4
56.4
63.5
764
73.8
34.3
79.9
711.2
8016
0.81
0.87
Avg.
62.0
61.7
72.5
53.5
52.4
58.9
71.0
70.0
$2.1
77.2
70.8
78.7
0.51
0,82
PAS
0,107
0.143
0,201
0.168
0.188
0.254
WinTR
77.2
65.3
84.4
85.7
0.64
0.78
DeT--mmall
CDTrass
DOT
24.9
75.6
72.5
72.4
76.4
77
60.6
63.7
56.7
61
59.1
64.1
79.5
$2.2
$4.3
81
85.5
96.7
$4.3
82.4
62.3
$4.4
84.8
0.68
0.63
0.76
0.79
M4,3
71.7
76.0
61.8
38.3
61.2
$1.7
81.2
R3.3
R2.5
K4.1
0.67
0.75
CDTrass
81.5
79.6
82
68.8
63.3
83.1
90.6
86.9
87.3
88.2
0.62
0.77
Dase
PMTrans
83
78.5
81.7
71.8
67.4
70.7
87.3
87.7
92
88.3
87.8
89.3
0.67
0.73
81.5
78.8
65.4
68.0
86.0
86.7
90.6
87.4
87.2
88.5
0.65
0.73
79.9
VT--Smill
SAMR
Arg.
80.1
80.2
79.8
78.8
W2.2
82.4
65.7
66:4
64.4
65.2
68.2
67
14.1
$4.2
$4.1
89.3
87.7
58.0
87.2
86.7
88.5
88.6
0.67
0.68
0.82
0.8
PAS
0.198
0.262
0.251
0.235
DuT
81.8
81.2
82.9
72.9
70.6
72.2
89.6
90.8
90.3
90.1
92.4
0.75
0.84
TYT
77.4
75.6
79.1
67.1
64.9
67.2
$3.5
$7.5
85.5
86.6
0.75
0.35
ViT-Buse
SSKT
BCAT
85.1
81.2
85
85.7
85.7
74.2
75.2
74.5
74.2
74,88
78.6
90.6
39
90.9
$5.3
92.1
91.8
90.9
91.1
89.9
90
90.8
91.3
0.74
0.76
0.83
0.87
PMTrans
82.4
0.84
Avg
PAS (our)
0.254
0.28
0.357
0.262
0.271
0.296
0.361
0.339
0.462
0.405
0.316
0.417
PMTrrans
88.4
87.9
89
81.3
90.4
80.9
92.9
93.4
94.8
92.8
93.2
93
0.75
0.72
Swin-Base
BCAT
88.6
87.4
86.7
75.3
73.7
75.4
90.3
93.5
92.9
92.7
92.5
0.68
0.74
(our)
14

---

Published as a conference paper at ICLR 2026
Table Target accuracy domain adaptation methods and transferability scores for the Office-31 dataset. The highest values are highlighted. Oracle baseline that considers the target labels.
Target
Correlation with PAS
Source
D
W
A
W
A
Pearson
Spearman
DANN
73.3
70.4
83.6
100.0
91.4
97.9
0.78
0.66
ADDA
69.6
72.5
90.0
99.7
94.6
97.5
0.67
0,60
BSP
74.1
73.8
88.2
100.0
92.7
97.9
0.75
0.66
DAN
66.9
87.3
100.0
84.2
98.4
0.83
JAN
69.2
71.0
89.4
100.0
93.7
98.4
0.70
0.60
CDAN
73.4
70.4
89.9
100.0
93.8
98.5
0.71
0.66
ResNet-50
MCD
68.3
67.6
87.3
100.0
90.4
98.5
0.76
0.66
AFN
72.9
71.1
94.4
100.0
94.0
98.9
0.67
0.83
MDD
76.6
72.2
94.4
100.0
95.6
98.6
0.65
0.66
MCC
75.5
74.2
95.6
99.8
94.1
98.4
0.66
0.83
FixMatch
70.0
68.1
95.4
100.0
86.4
98.2
0.75
0.83
Avg.
71.1
70.0
89.6
99.9
90.6
98.1
0.73
0.66
PAS (our)
0.265
0.239
0,286
0.454
0.236
0.423
TRANS-DA
77
77.1
94.8
100
95.8
98.8
0.69
0.71
CDTrans
78.4
78
94.6
99.6
93.5
98.2 2
0.74
0.94
DeiT-Small
Avg.
77.7
77.6
94.7
99.8
94.65
98.5
0.72
0.94
PAS (our)
0.283
0.266
0,304
0.472
0.278
0.447
CDTrans
81.1
81.9
97
100
96.7
99
0.69
0.83
PMTrans
81.4
82.1
96.5
100
99
99.4
0.64
0.71
DeiT-Bar Base
Avg.
81.3
96.8
100.0
97.9
99.2
PAS (our)
0.268
0,241
0.304
0.443
0.251
0.418
SSRT
83.5
82.2
98.6
100
97.7
99.2
0.61
0.94
ViT-Small
PAS (our)
0.283
0.27
0.302
0.509
0.276
0.473
DoT
85.1
86.8
96.7
100
96.6
99.4
0.74
0.83
TVT
84.9
86.1
96.4
100
96.4
99.4
0.75
0.75
SSRT
79.2
79.9
95.8
100
95.7
99.2
0.72
0.83
ViT-Base
BCAT
84.9
85.8
97.5
100
96.1
99.1
0.73
0.83
PMTrans
85.7
99.4
99.1
99.6
0.62
0.83
Avg.
84.0
85.0
97.2
100.0
96.8
99.3
0.71
0.83
PAS (our)
0.423
0.395
0.453
0.59
0.412
0,558
TMTrran
86.7
86.5
99.8
100
99.5
99.4
0.61
0.83
BCAT
85.7
86.1
99.6
100
99.2
99.5
0.63
0.89
Swin-Base
Avg.
86.2
86.3
99.7
100
99.4
99.5
0.62
0.89
PAS (our)
0.361
0.349
0.399
0.589
0.374
0.56
15

---

Published as a conference paper at ICLR 2026
Table 8: Target accuracy domain adaptation methods and transferability scores for the Image- CLEF dataset. The highest values are highlighted. Oracle baseline that considers the target labels.
Target
Correlation with PAS
Source
C
Pearson
Spearman
RTN
95.3
92.2
86.9
86.8
72.7
75.6
0.29
0.49
MADA
96.0
92.2
88.8
87.9
75.2
75.0
0.20
0.26
ICAN
94.7
92
89.9
89.7
78.5
79.5
0.23
0.49
CDAN-E
97.7
94.3
90.7
74.2
77.7
0.27
0.49
SymNets
97.0
96.4
93.4
93.6
78.7
80.2
0.17
0.60
MEDA
95.7
95.5
92.2
92.5
78.5
79.7
0.16
0.60
ResNet-50
SPI.
96.7
96.3
95.7
94.5
80.5
78.3
0.02
0.26
DS-c
92.8
91.3
87.3
86.7
70.4
78.7
0.39
0.49
CAN
95.5
95.2
91.6
91.8
76.4
78.5
0.19
JAN
94.7
91.7
89.5
88.0
74.2
76.8
0.24
0.49
CDAN
98.3
94
90.7
88.3
76.7
77.2
0.22
0.49
Avg.
95.9
93.7
90.7
90.0
76.0
77.9
0.22
0.49
PAS (our)
0.299
0,251
0.235
0.27
0.223
0.297
TRANS- DA
97.5
97.5
93.7
95.2
78.3
80.8
0.41
0.52
DeiT-small
PAS (our)
0.344
0.303
0.263
0.322
0.24
0.332
VT-ADA
97.3
96.0
96.2
94.1
78.9
81.8
0.55
CSTrans
98.2
98.2
97.0
97.2
80.0
82.0
0.54
0.62
ViT-Base
Avg.
97.8
97.1
96.6
95.7
79.5
81.9
0.55
0.54
PAS (our)
0.399
0.359
0.304
0.377
0.262
0.363
Table Target accuracy of domain adaptation methods and transferability scores for the DomainNer dataset. The highest values are highlighted. Oracle baseline that considers the target labels.
Target
Correlation
Source
C
Peursom
Spearnan
DANN
41.7
50.7
55.0
37.9
35.6
0.53
0.46
JAN
47.2
54.2
36.6
40.5
52.6
45.2
36.7
59.9
45.1
43.0
41.9
0.63
0.58
CDAN
45.1
55.6
57.2
40.4
53.6
45.4
56.8
58.4
55.7
46.1
40.5
43.0
0.60
0.50
R0
MCD
44.5
52.0
55.5
37.5
51.5
44.5
52.9
34.5
$2.0
41.0
41.6
39.7
0.57
0.47
MDD
48.6
58.3
38.7
53.7
42.6
0.60
0.59
Avg.
45.5
53.7
56.7
30.4
52.2
45.8
35.9
SXL1
44.8
40.7
41.0
0.58
0.53
PAS (our)
0.108
0.145
0.088
0.08
0.159
0.083
1.188
1.1
1.107
0.088
0.098
0.114
WinTk
53.2
20.5
51.6
62.0
71.3
50.1
63.1
55.9
45.
65.3
0.32
0.51
DeT--mmll
CDTRANS
52.5
Ave.
52.3
68.8
52.2
58.6
69.7
48.4
62.3
56.6
48.5
64.7
$2.4
67.2
0.39
0.57
PAS (our))
0.13
0.152
0.093
0.091
(1175
0.086
0.139
0.218
0.11
0.096
0.117
0.127
53.6
21.2
61.8
72.2
52.9
0.27
0.45
Duse-Buse
WINTR
56.3
72.8
57.3
69.2
74.4
55.6
68.2
59.8
55.1
69.9
58.
73.1
0.20
0.39
Avg
55.7
72.2
36.9
61.6
72.9
$3.3
65.3
59.4
$2.4
68.7
36.7
71.8
0.26
0.44
PAS (our)
B.126
0.147
0.086
0.885
I.165
B.779
0.137
0.211
0.102
0.089
0.119
0.112
NAXIS
77.8
0.38
ViT-Rose
SSKT
60.2
75.8
59.8
61.7
71.4
55.2
69.9
66.0
58.9
70.6
62.2
73.2
0.50
0.46
AVE
60.7
77.7
60.7
66.2
75.9
57.2
69.7
61.6
$7.9
71.4
62.7
76.3
0.37
0.35
PAS (owr)
0.185
0.226
0.162
0.145
0.233
0.128
0.233
0.282
0.176
0.151
6.171
0.17
16

---

Published as a conference paper at ICLR 2026
(a) Predicted: Car |True Bottle
(b) Predicted: Horse |Tree: Per-
(c) Predicted: Motorcycle |True
Car
Segtinented
(d) Predicted: Plane True:
(e) Predicted: Person [Tree; Bot- tle
(f) Predicted: Dog True: Bird
Canopies
(g)
Predicted: Bike True: Bus
(h) Predicted: Car True: Person
(i) Predicted: Motorcycle |Tree:
Person
Figure 6: Examples of images misclassified by the domain adaptation method DANN in the dataset (Pascal VOC 2012) of the ImageCLEF benchmark
17


Reviews:
[{"review_id": "14GkWAH4p0", "reviewer": "Reviewer_YdsH", "summary": "The paper proposes a simple score to predict how well a pre-trained model will perform when adapted to a new domain. The score is based on distances to class centroids. Experiments show that the score correlates with target domain performance.", "questions": "* What correlation coefficient is used in the paper? Spearman's rank correlation would be more appropriate than Pearson's correlation, because the relation between PAS scores and target accuracy might be nonlinear, and the order is probably most important.\n* Why is the PAS score using (d2-d1)/d2? I don't see any motivation for this choice.\n* Table 1: What domain adaptation method is this using? Or are you simply applying model on target?\n* In my opinion, the pseudo code in Algorithm 1 doesn't add much to the paper, since the explanation in Eq (1)-(3) is much more clear, and the pseudo code does not match how the algorithm would be implemented in a modern framework. In addition, the placement in the paper makes the surrounding text harder to read.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "* The proposed method is clearly explained, and should be easy to implement.\n* Experiments show a good correlation between PAS score and target accuracy.\n* The experiments use standard datasets for domain adaptation.\n* I did not find any spelling or grammar errors.", "weaknesses": "* There is no comparison to related work.\n  * The paper claims that \"To the best of our knowledge, this is the first proposal for transferability estimation for the domain adaptation setting.\" But there have actually been investigations into this before. In particular there is the Proxy A-distance (Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira; Analysis of representations for domain adaptation; NIPS 2006), and there should be a comparison with this A-distance.\n  * The paper also makes no comparison to methods that first perform domain adaptation and then try to estimate performance, because they claim that these methods are too costly. However, several domain adaptation methods only require a single evaluation of the source model, followed by a relatively cheap adaptation procedure. This should be comparable in cost to the proposed PAS score. An experimental comparison with these methods must be made, which can include a comparison of the computational cost.\n* Figure 3 combines multiple datasets, to show a correlation between PAS score and target accuracy. But there are large differences between the different datasets, while the within dataset correlation is perhaps less clear. It is not appropriate to compute a single correlation coefficient for the combined datasets.", "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": "tyGPnLocAE", "reviewer": "Reviewer_j93r", "summary": "This paper proposes a new method to estimate the right selection of pre-trained checkpoints and source domains for achieving highest accuracy on a particular target domain. Specifically, they rely on the ratio of the distance between a target samples and the nearest and second-nearest source prototype as a measure of transferability. Though simple, this measure is very intuitive and equally effective in the experiments. Results across various domain transfers show the string correlation between PAS score and eventual DA accuracy.", "questions": "1. The experiments currently do not include the choice of which source domain is most suitable for a given target domain, although this is presented as one of the motivations in the introduction. This ablation will be helpful to have in the paper. \n\n2. It is not completely clear how the centroid of the features are calculated in Eq1. Isn't centroid just the average of the vectors? Why is there an additional normalization in the denominator?\n\n3. As it is mentioned that this method can also be used to choose which source domain matches a particular target domain, it would also be helpful to show why we can't just use _all_ the source domains instead of specifically choosing the best. \n\n4. It is not clear if this method will be applicable to open-world domain adaptation methods and what modifications need to be performed to adapt this to a more general setting.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. The paper addresses a very important problem of estimating the right choice of source domain and pre-trained model + architecture while faced with optimizing accuracy on an unlabeled target domains with significant domain shift. \n\n2. The presented hypothesis is very simple and easily usable in wide variety of scenarios indicating the strong effectiveness of the approach. \n\n3. The distance measure presented in Eq 3 will be small if either (i) the target sample is equidistant from two source classes (poor choice of source domain), or (ii) the two source classes are very close to each other (poor discriminability of source features), covering both the choice for source domain and source pre-trained model efficiently well. \n\n4. The strong correlation of PAS score with various transfer settings verifies the effectiveness of proposed hypothesis.", "weaknesses": "1. The experiments only consider very basic transfer datasets which are relatively small scale for modern DA. Larger-scale datasets like DomainNet [1] and GeoNet [2] are not covered limiting the insights we can draw from this work for practical use-case settings. \n\n2. There are no sufficient ablations provided in the experiments regarding other potential measures of transferability. For instance, one measure could just be the average distance between the feature representations of source and target domains in the feature space, and improvements over this simple baseline setting could be helpful. \n\n\n[1] Peng, Xingchao, et al. \"Moment matching for multi-source domain adaptation.\" Proceedings of the IEEE/CVF international conference on computer vision. 2019.\n[2] Kalluri, Tarun, Wangdong Xu, and Manmohan Chandraker. \"Geonet: Benchmarking unsupervised adaptation across geographies.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.", "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": "CvEXCoCh3J", "reviewer": "Reviewer_mW8M", "summary": "This paper studies an interesting and important problem in transfer learning, specifically unsupervised domain adaptation (UDA) — the assessment of source model transferability. To address this, the authors propose a Potential Adaptability Score (PAS), which measures the compatibility between source and target domains based on the generalization capacity of pre-trained models. PAS leverages pre-trained feature embeddings to quantify the potential adaptability across domains. Although the proposed metric is conceptually interesting and somewhat novel, a major concern lies in the limited discussion and comparison with existing transferability indicators. The authors claim that no comparable baselines exist. However, several existing indices, though possibly less effective, could serve as meaningful references, for example feature distribution discrepancy measures (Maximum Mean Discrepancy, Wasserstein distance) and prediction uncertainty.", "questions": "Please refer to the weakness section.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "-\tThe paper is well organized and clearly written.\n-\tThe paper targets transferability in domain adaptation, which is both interesting and relevant to the ICLR community.\n-\tThe proposed PAS index provides a potentially useful tool for estimating transferability in UDA scenarios.", "weaknesses": "-\tSome claims in the manuscript appear overstated. For instance, Lines 108–109 state that “this is the first proposal for transferability estimation for domain adaptation.” However, there exist several prior works on transferability estimation, such as NCE, LEEP, and LogME. While these studies are mentioned in the related work section, the claim of being the first proposal is inaccurate, as these methods also target transferability and can be adapted for domain adaptation settings.\n \n-\tAnother overstatement appears in the experimental design section (Line 313), where the authors argue that “there is no comparable baseline.” This assumption is not entirely fair. Several existing metrics, such as Maximum Mean Discrepancy, Wasserstein distance, and the averaged prediction entropy over target data, could serve as comparable baselines to evaluate the proposed PAS. Including these in the experiments would make the comparison more convincing and substantiate the claimed superiority of PAS.\n\n-\tThe paper would benefit from a deeper analysis, particularly in cases where PAS fails to identify the most suitable pre-trained model. Discussing such failure cases could provide valuable insights into the limitations and applicability of the proposed metric.", "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)