Seeing Beyond Labels: Source-Free Domain Adaptation via Hypothesis Consolidation of Prediction Rationale
Authors: Yangyang Shu
Yuhang Liu
Xiaofeng Cao
Qi Chen
Bowen Zhang
Ziqin Zhou
Anton van den Hengel
Lingqiao Liu
Venue: Accepted by TMLR ()

Abstract: Source-Free Unsupervised Domain Adaptation (SFUDA) is a challenging task where a model needs to be adapted to a new domain without access to target domain labels or source domain data. The primary difficulty in this task is that the model's predictions may be inaccurate, and using these inaccurate predictions for model adaptation can lead to misleading results. To address this issue, this paper proposes a novel approach that considers multiple prediction hypotheses for each sample and investigates the rationale behind each hypothesis. By consolidating these hypothesis rationales, we identify the most likely correct hypotheses, which we then use as a pseudo-labeled set to support a semi-supervised learning procedure for model adaptation. This approach distinguishes itself from conventional semi-supervised learning by relying solely on pseudo-labels rather than ground-truth annotations. To achieve the optimal performance, we propose a three-step adaptation process: model pre-adaptation, hypothesis consolidation, and semi-supervised learning. Extensive experimental results demonstrate that our approach achieves state-of-the-art performance in the SFUDA task and can be easily integrated into existing approaches to improve their performance. The codes are available at \url{https://github.com/GANPerf/HCPR}.

Yuhang Liu Published in Transactions on Machine Learning Research (06/2025)
Seeing Beyond Labels: Source-Free Domain Adaptation via Hypothesis Consolidation of Prediction Rationale
Yangyang Shu
yangyang.shuu@unne wwwp::/ww....nosssss.s.....
School of Systems and Computing, University of New South Woles
yuhang. liu01 @adelaide. edwiiddeeedda..o..
Australian Institute for Machine Learning, The University of Adelaide
Xiaofeng Cao
aicoffengaao@ttnngii.d..cc
School of Computer Science Technology, Tongji University
Qi Chen
gi.chen04 @adelaide edu.ov
Australian Institute for Machine Learning, The University of Adelaide
Bowen Zhang
b. b.shang@adelaide..c www.dellladeee......
Australian Institute for Machine Learning, The University of Adelaide
Ziqin Zhou
zigia.chou@@adeladddeeco
Australian Institude for Machine Learning, The University of Adelaide
Anton van den Hengel
Australian Institute for Machine Learning, The University of Adelaide
Lingqiao Liu"
linggiao liu @iw@allldde edu.
School of Computer Science, The University of Adelaide
Reviewed on OpenReview: https: openreview. net/ forum? id- 3997
Abstract
Sourco-Free Unsupervised Domain Adaptation (SFUDA) is a challenging task where a model needs to be adapted to new domain without access to target domain labels or source domain data. The primary difficulty in this task that the model's predictions may be inaccurate, and using these inaccurate predictions for model adaptation can lead to misleading results. To address this issue, this paper proposes novel approach that considers multiple prediction hypotheses for each sample and investigates the rationale behind each hypothesis. By consolidating these hesot rationales, we identify the most likely correct hypotheses, which we then use as a pseudo-labeled set to support a semi-supervised learning procedure for model adaptation. This approach distinguishes itself from conventional semi-supervised learning by relying solely on pscudo-labeis rather than ground-truth annotations. To achieve the optimal performance, we propose three-step adaptation process: model pre-adaptation, hypothesis consolidation, and semi-supervised learning. Extensive experimental results demonstrate that our approach achieves state-of-the-art performance in the SFUDA task and can be casily integrated into existing approaches to improve their performance. The codes are available at hhtps:: pettps://githhe //githubb com /Amm//NNeer//CRRR
*Corresponding author.

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Published in Transactions on Machine Learning Research (06/2025)
1 Introduction
The success of deep learning models visual tasks is largely dependent on whether the training and testing data share similar distributions (He et al., 2016 Liang et al. 2020b). However, when the distribution of the testing data differs significantly from that of the training data, also known domain shift, the performance of these models can decrease substantially TTeeng et al. 2017 Peng et al. 2019). To mitigate the effects of domain shift and reduce the need for data annotations, Unsupervised Domain Adaptation (UDA) techniques
have been developed to transfer knowledge from annotated source domains to new but related target domains
without roquiring annotations in the target domain Hoffman et al. (2018) Long et al.]
[2018) Dai et al. 2020);
Feng et al. 20021) Mei et al. (2020). However, most. UDA-based methods rely on access to labeled source domain data during adaptation, such an access may not always be feasible due to privacy concerns. As a
result, Source-Free Unsupervised Domain Adaptation (SFUDA) Liang et al. (2020a); Yang et al. (2021b a);
Chen et al. (022): : Yang et al. (2022) Zhang et al. (2022) Karim et al. (0023) gains much attention recently,
which only requires pre-trained model from the source domain and unlabeled data from the target domain.
The main challenge in SFUDA research is how to generate supervision solely from unlabeled data. The current approaches in SFUDA research primarily focus on either generating pseudo-labels Liang et al. (2020a): )
Yang et al. [2021b ; Litrico et al. (2023) or conducting unsupervised feature learning Huang et al. (2021 ;
Chen ct al. (2022); Zhang et al. (2022); Karim et al. (2023): Litrico et al. (2023) address this issuc. To
generale reliable pseudo-labels, existing methods Liang et al. (2020a); Yang el al.
a often utilize the distribution of the target domain data to refine the initial predictions from the source domain, ie., via
(2021b
clustering Liang ct al. (2020a) or using the predictions of neighboring samples Yang ct al. [2021a] 9 Litrico et al.
(2023). On the other hand, unsupervised feature learning, such as contrastive learning, is often employed as
an auxiliary task to encourage the features to adapt to the target domain Huang et al. (0021) Chen et al.
2022 Zhang et al. 2022): Karim et al. (2023): ; Litrico et al. (2023).
In our study, we propose a novel approach to tackle the challenge of SFUDA. Our strategy involves deferring the utilization of label predictions to update the model in the early stages and carefully selecting the most
reliable predictions to construct a pseudo-labeled set. The key innovation of our approach lies in considering multiple prediction hypotheses for each sample, accommodating the possibility of multiple potential labels for each data point. We treat each label assignment as a hypothesis and delve into the rationale and supporting evidence behind each prediction. We utilize a representation derived from GradCAM Sclvaraju et al. (011)) to encode the rationale for predicting an instance to a hypothetical label. Our methodology is inspired by the belief that assessing the correctness of a prediction can be more reliable by analyzing the reasoning behind particular prediction, rather than solely relying on prediction probabilities. Subsequently, we develop consolidation method to determine the most trustworthy hypothesis and utilize it as the labeled dataset in semi-supervised learning framework. By employing this technique, we effectively transform the SFUDA problem into a conventional semi-supervised learning problem.
Concretely, our approach consists of three key steps: semi-supervised learning. We have empirically observed that pre-adapting the model can enhance the effectiveness of the second step. To accomplish this, we introduce a straightforward objective that encourages prediction smoothness from the network. In the final step, we leverage the widely-used FixMatch (Shnn et al. (2020) algorithm as our chosen semi-supervised learning method. Through extensive experimentation, we demonstrated the clear advantages of our approach over existing methods in the SFUDA domain and show that the proposed method can be easily integrated into existing approaches to bring improvement.
model pre-adaptation, hypothesis consolidation, and
2
Related Work
2.1 UDA
Unsupervised domain adaptation aims to transfer knowledge learned from a labeled source domain to an unlabeled target domain. Various approaches have been proposed to address this task, including discrepancy
minimization Tzeng et al. (2014); Ganin & Lempitsky (2015) Long et al. (2015), adversarial Icarning Hoffman
et al. 2018: Long et al. (2018) Tzeng et al. (2017) Vu et al. 2019), contrastive learning Dai et al. (2020):
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Kang et al. 2019 and methods grounded in causal perspective Liu et al. (0255) that seek to identify heve-level latent causal variables Liu et al. (2024a 2022 2024b Recently, sell-training using labeled source
data and pseudo-labeled target data has emerged as prominent approach in unsupervised domain adaptation (UDA) rescarch Feng ct al. (2021): |: Mci et al. (2020); Xic et al. (2020); Yu et al. (2021): Zou et al. (2018).
However, these methods typically rely on access to the source data, making them inapplicable when source data unavailable.
2.2 SFUDA
Sourco-free unsupervised domain adaptation involves adapting pre-trained model from a source domain to
target domain without access to souree data+labeih or target labels Li et al. (2024); Fang et al.
2024 Zhang
et al. (2024). Existing SFUDA methods can be broadly categorized into two classes: i) Label Refinement:
Methods such as SHOT Liang et al. (020a)), G-SFDA Yang et al. (0221b), NRC Yang et al. (022aa), and
GPL Litrico et al. (2023) focus on refining pscudo labels. SHOT generates pscudo labels using centroids obtained in unsupervised manner. G-SFDA, NRC, and GPL refine pseudo labels through consistent predictions and nearest neighbor knowledge aggregation from local neighboring samples. ii) Contrastive Feature Learning: Approaches like HCL Huang et al. (2021), C-SFDA Karim et al. (2023) AdaContrast Chen et al. (2022), GPL Litrico et al. (2023). and DaC Zhang et al. (2022). HCL and C-SFDA use contrastive loss similar to moco He et al. (2016), where positive pairs consist of augmented query samples and negatives are other samples. AdaContrast and GPL exclude same-class negative pairs based on pseudo labels. DaC divides the larget data into source-like and larget-specific samples, compules source-like class centroids, and generates negative pairs using these centroids. These methods aim tackle SFUDA by refining pseudo labels leveraging contrastive feature learning, demonstrating the potential of different strategics in addressing the challenges of adapting models without access to labeled source data or target label.
3 Method
Correctly prodicted samples
Incorrectly predicted samples
'loptop" images
GradCAM for 'laptop' Prof: Haptop'
GraCAM for "keyybardd' Prod: 'keyboard"'
GradCAM for 'aptapp
"clock" imirges
ON
"clock" Predi "clock
GriCCAM "sign" Prod: 'sign"
GradCAM "closk"
Figure 1: The visualizations illustrate the GradCAM Sclvaraju et al. (2017) for predicting the image to a specific class. In the right-half section, it can be observed that even though the prediction is incorrect, the obtained rationale (region highlighted the GradCAM) based on the correct label remains reasonable and resembles the rationale of the corresponding class depicted in the left-half section.
the source-free free unsupervised domain adaptation (SFUDA) setting, only pretrained source models and unlabeled data the target domain are given. The task is adapt the model to the target domain by using unlabeled target data only. Our approach sequentially applies three steps as described in Sec. 3.1j. Sec. 3.2 and Sec. 3.3
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Multiple Prediction Hypotheses for Fach Sample
Rationale Representations Distribution in Target Domain
hypothesis
(4 y=clock)
Q1
toys
hypothesis
11 y=yys)
02
ed2
hypothesis
1,, sign)
03
sign
hypothesis
(12- y-clock)
04
clock
hypothesis 5
U2. yoyy))
a5
hypothesis 6
(12, sign)
ag rationale representation with different classes
class-wise rationale
Figure 2: In our method, we generate multiple prediction hypotheses based on the posterior probability of the current model. An image / and its hypothetical label form a hypothesis, for example, (4, y clock). For each
hypothesis, GradCAM is calculated based on the hypothetical label, resulting in the corresponding rationale reprosentation a. Subsequently, we calculate the centroid for the rationale representation of each class.
3.1 Model Pre-adaptation via Encouraging Smooth Prediction
The first step of our approach is to make an initial adaptation to reduce the domain gap. The motivation for introducing this pre-adaptation phase arises from the observation that early predictions from a source model can be noisy and overconfident due to domain shift, which negatively impacts the reliability of downstream pseudo-labels. To mitigate this, we hypothesize that enforcing prediction smoothness across the target data manifold helps the model form more consistent and transferable representations. Our method first encourages alignment among similar samples and separation among dissimilar ones stabilize the model create memory Q E igd to store N₂ randomly sampled image
before greudo-labeling | Specifically, embedding and update il after each batch training. Then for each largel sample I;, we find the z-nearest. neighbor (x;)); and z-samples FN(xx) that are furthest to z based on the Euclidean distance between the image embedding of x, and embedding in Q (d = 256 and 2 = 3 in our implementation). Then we optimize the following objective:
Ng Σ
NB
LPA LsM + XLFAR
KL(p(z)). ((ol 1= (
Σ
Σ
Σ EFF
p(a:))
(1)
where p denotes the posterior class probability predicted by the source model after applying softmax to the logits, and KL, represents Kullback-Teibler divergence is computed between the softmaxed class probability distributions of two target samples x; and 2/ as measure of distributional similarity. Ng is the number of samples within mini-batch. The first term is used to ensure similar samples have similar predictions. However, using the first term alone may lead to trivial solution that assigns identical prediction for every instance. Thus we use the second term to counter-act it as it ensures that the least similar samples should have divergent posterior probabilities, iee, the inner product between posterior should close to zero.
3.2 Hypothesis Consolidation from Prediction Rationale
After pre-adaptation, the model generally exhibits improved adaptation to the target domain. However, there may still be instances where the model produces incorrect predictions, making challenging to rectify
Otter pre-adaptation approaches may also work, such as the method Llang et 2020a please experimental evidence.
4.6 more
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Published in Transactions on Machine Learning Research (06/2025)
misclassifications solely based on predicted posterior probabilities. Therefore, in the second step, we explore more robust methodology for analyzing predictions.
We begin by considering multiple prediction hypotheses for each individual instance. Specifically, for each instance, we consider the top classes with the highest posterior probabilities as potential prediction hypotheses, denoted as (1a.1/k k E top k. In other words, we acknowledge the correct class label could exist. within one of these top k classes, even though we do not know which one.
To further analyze each hypothesis (zz, (e), we calculate the GradCAM Selvaraju et al. (017) to identify the regions that contribute to supporting the prediction for v resulting in a. representation called the rationale representation agk. This rationale representation encodes the evidence supporting the corresponding hypothesis. Drawing inspiration from prior work Shu el al. 2022 2023), we formally calculate dik using following equation:
([
(logit()))) alo()))mm.
Σ
(2)
HW
where au E Rd,, ((x;) E #xx"" xd' is the feature map of the last convolutional layer of the network with H height, W width, and d' channels. [o(xx)]m, E Rdd is the feature vector located at the n)-th grid.
(vAht is the logit for class vit [++ max(-,)) 0).
[d(z;)) is equivalent to GradCAM 3lo(a))m...
alogit(ghi)
value at the (m, n)-th grid. Essentially, the calculation of aak performs weighted average pooling over d(;;) according to the GradCAM. Figure [ shows the GradCAM calculated from different hypotheses for the same image. Upon observation, we notice that, even if the ground-truth class is not ranked as the top prediction by the model, its associated rationale remains reasonable and similar to the common rationale patterns for the corresponding class. This inspires to leverage this observation to analyze the model's current predictions. For example, if an instance has a prediction hypothesis that exhibits a rationale similar to the corresponding class's common rationale but is not ranked as the top prediction, then the top prediction may not be correct.
Formally, we calculate the class-wise rationale centroid as the average rationale representation from cach hypothetical class, representing the common rationale for each class:
ā. = Eis 11it = c)ask En 1(12 c)
33)
where e represents class and [v c) = 1 if k c. The idea of using multiple hypotheses with the
rationale representation is illustrated in Figure 2
Next, we generate a ranking index rik for each prediction hypothesis (I,, aas, v by ranking the Euclidean distance between 0g and its corresponding rationale centroid a i.c., the centroid for class y in the ascending order. For each instance Ii, we obtain i ranking indices rik, k E top k classes, one for each hypothesis. Then, a hypothesis {I;, Vik' } is considered reliable if it satisfies the following two conditions: (1) ras < Tı indicating the rationale for (x;, Jar) is typical as its rationale representation is close to the rationale centroid. (2) Tiy > T2 Vj * K', where T2 > Ti are two predefined ranking thresholds. The second condition ensures that there are no conflicting hypot i.e, no other hypothesis is likely to be true for the same instance as their rationale appears to be unusual.
With those criteria, we can collect a set of reliable hypotheses P as samples with their corresponding hypothetical labels. Representative examples of this procedure are depicted in Figure 3 It is important to note that in the second step, aim to select the most reliable hypothesis rather than correcting hypotheses. This is because we believe that the task of correcting predictions or hypotheses can be better accomplished through the use of semi-supervised learning, which allows for the gradual propagation of pseudo-labels.
By focusing on identifying the most reliable hypothesis based on the proximity of the rationale representation to the rationalc centroid and the absonce of conflicting rationales, we can create a high-quality set of pscudo-labeled samples (see Section 4.5p. These pseudo-labels can then be used in semi-supervised learning framework to refine the model's 'ss predictions and gradually improve its performance.
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T1 = 400
T2 =
(11, a1. Y1)
rank 90
T2
Case
(/1, a2. Y/2
rank =
<1
Reliable
(11 a3. V/))
rank 200
> 12
(l2, a4. Y1)
rank
<Tp, Tz
Case 2
(12, as. Y/2
rank =
<t < T2
UnReliable
(12 96- Ya))
rank = 100 > >12
(13, a7 Y1)
rank 100
T1 > T2
Case
(13, ag. Y/2)
rank 120 > T1 > T2
UnReliable
(13, ag. V3)
rank 150 > >1, >
Figure 3: These examples demonstrate the generation of reliable hypotheses. In Case 1, the rank ID of the second hypothesis derived from the image is lower than 7i, while all other hypotheses from the same image have ranks larger than Consequently, the second hypothesis of I is selected as a reliable hypothesis. In Case 2, no hypothesis is selected because it has two hypotheses with rank IDs less than T2, indicating a conflict between those hypotheses. Similarly, Case 3 is not selected because none of its hypotheses has rank IDs lower than T1-
3.3 Semi-Supervised Learning
After completing the second step of hypothosis consolidation, we obtain a reliable psoudo-label set P, while the remaining samples are treated as the unlabeled set U. At this stage, we are ready to apply semi-supervised algorithm to perform the final step of adaptation. For this purpose, we utilize one of the seate-of-the-ar semi-supervised mothods, FixMatch Sohn al. (2020). which combines consistency regularization and pscudo-labeling to address this task.
Specifically, start by sampling labeled mini- batch B, from the reliable pseudo-label set P and unlabeled batch B, from the unlabeled set U. We then optimize the following objective function using these batches:
CFM = Σ CE p( + Σ ZyES,
(max (p(Aw 2 CE p(A..)))()))) zu
(4)
Jr EB
where 0n = arg max ply = ((w (.) and A.(.) are the weakly-augmented and strongly-augmented
operations, respectively. is the threshold defined in FixMatch identify reliable pseudo-label (we the same with FixMatch 0.95), and CE is the cross-entropy between two probability distributions.
We present the overall training process of our proposed SFUDA method in Algorithm
4 Experiments
4.1 Datasets
Office-Home Venkateswara et al. (2017) consists of 15,500 images categorized into 65 classes. It includes four distinct domains: Real-worlh (Rw), Clipart (CI), Art. (Ar), and Product (Pr). To evaluate the proposed method, researchers perform 12 transfer tasks on this dataset, involving adapting models across the four domains. The evaluation reports cach domain shift Top-1 and the average Top-1 accuracy. Originally, the NomainNet dataset Peng et al. (2019) consisted of over 500,000 images, including six domains and 345 classes. For our evaluation, follow the approach described in Saito et. al. (2019) and focus four domains:

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Published in Transactions on Machine Learning Research (06/2025)
Algorithm 1 SFUDA with Hypothesis Consolidation of Prediction Rationale
Require: Unlabeled target data D., pro-trained model, memory Q stores N₂ randomly sampled image embedding, two ranking thresholds 71 and T2 the number of steps of updating model pre-adaptation K1, the number of steps of updating semi-supervised learning K2.
for K steps do Sample a cani-batch of Ng training data {aa from D,, find z-nearest neighbor (a( and æ-furthest neighbor (x,).
Update model by Eq. 1.
Updale memory Q
end for
For each sample 2; in D,, caleulate rationale reprosentation of cach class via Eq. 2.
Calculate class-wist wise rationale centroid for each class via Eq. 3.
Collect a set of reliable hypotheses with their corresponding hypothetical labels as reliable pseudo-label set P, and the remaining samples as the unlabeled set U.
for K2 steps do
Sample labeled mini-batch B3 from P and an unlabeled batch Bg from U.
Update model by Eq. 4. end for
Real World (Rw), Sketch (Sk), Clipart (CI), and Painting (Pt). We assess our proposed method on seven domain shifts within these four domains. VisDA-C Peng al. (2017) contains 152,000 synthetic images from the source domain and 55,000 real object images from the target domain. It consists of 12 object classes, and there is significant synthetic-to-real domain gap between the two domains. Our evaluation reports per-class Top-1 accuracies, as well as the average Top-1 accuracy on this dataset.
4.2 Implementation Details
To ensure fair comparisons with previous work Liang al. (020aa); Chen et al. 2022 Karim et al. (0023
employ the ResNet-50 He et al. (2016) as the network backbone for the OfficeHoome and DomainNel datasets, and ResNet- 101 for the VisDA-C dataset. The network architecture follows the same configuration as SHOT Liang et al. (2020a). Specifically, we replace the original fully connected (FC) layer in ResNet-50/ 101 with a bottleneck layer of 256 dimensions and apply batch normalization loffe & Szegedy (2015). This modified setup serves as the feature +erractoo+prooecee head, producing feature representations and embedding of 256, respectively. Additionally, we include an extra fully connected layer with weight normalization Salimans & Kingma (2016) as task-specific classifier.
dimensions d' 2048 and
In the first step of model pre-adaptation, we use batch size of 64. The value of À is set as À = (1-01+00-7 -5 where Ao = 1, and p' represents the training progress variable ranging from 0 to 1, calculated as max äter In the second step of hypothesis consolidation, we set the number of nearest nearest/furthert /furthest neighbor per instance 2 as 3, and set hepothesis per instance & as 4, respectively. The ranking thresholds Tı and T2 are determined as a percentage of the total number of samples on the three datasets, specifically set at 0.8% and 1.6%. In the third step of semi-supervised learning, we set the size of B, and B, to 64.
iter
We use the SGD optimizer with momentum of 0.9 and weight decay of 1e-3 for all datasets. The learning rate is set as 1e-4 for all datascts, except for the bottleneck layer and the additional fully connected layer, where it is set as -3 We train for 40 epochs on the OlliceHHome and NomainNet. datasets, where 9 epochs are dedicated to the model pre-adaptation. For the VisDA-C dataset, we train for 15 epochs, with 7 epochs allocated for the model pre-adaptation. All images from the datasets undergo augmentation, including weak and strong augmentation. Weak augmentation involves a standard flip-and-shift augmentation strategy, while strong augmentation is similar to the approach used in the work of Sohn et al. (2020).
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Table 1: Accuracy (%) on medium-sized Office-Honnd dataset (ResNet-50). "SF" denotes source-free, We highlight the best results.
Meshod
8P
Ar-+CI
Ar-sPf
Ar-sRtw
CI-AAr
Cl-+P
Ci-RR
Pr-AA
Pr-+Cl
Pr-RRw
Rw-+Ar
Rw-+CI
Rw--Pt
Avg.
37.4
38-3
60-4
-1.2
GSDA
2a A6
61.3
76.1
79.4
65.4
73.3
74.3
65.0
53.0
$0.0
72.2
60.6
83.1
70.3
RSDA
53.0
77-1
81.3
66.4
74.0
76.5
07.9
53.0
82.0
75.8
57.8
85.4
70.9
SRDO
bang
52.3
76.3
81.0
68.5
76.2
78.0
08.7
53.8
81.7
76.3
57.1
85.0
71.3
PicHi
Na
2021
38.1
77.5
80.4
67.7
79.5
78.1
65.8
57.9
81.7
76.4
02.9
86.7
72.7
G-SF1A
78.6
66.7
77.2
77:2
65.6
56.0
82.2
72.0
57.8
71.3
SHIOT
Linin nt.
36.9
78.1
81.0
67.9
78.4
78.1
67.0
64.6
$1.3
73.4
58.1
84.5
71.6
Lintig
60.7
NRC
Your st.
X121
57.7
80.3
82.0
68.1
79.8
78.6
65.3
56.4
$5.0
71.0
58.6
85.6
72.2
600.5
HCT
Trway
64.0
78-6
82.4
64.5
78.
80.1
64.8
69.8
75.3
78.1
69.9
81.5
72.6
AnD
Yang
39.0
79.3
68.9
79.8
79.5
67.2
57.4
SA.1
58.5
85.4
72.7
DaC
Ziang
59.1
79.5
81.2
69.3
78.9
79.2
07.4
36.4
82.4
74.0
61.4
81.1
72.8
VMP
37.8
77.6
82.5
68.6
79.4
80.6
68.4
65.6
83.1
75.2
59.6
84.7
72.8
79.5
92.4
60.77
78:2
82.6
60.8
72.9
C-SFDA
Karin of
10/25
60.3
80.2
82.9
69.3
80.1
78.8
67.3
68.1
80.4
73.8
61.3
86.3
73.5
Ours
30.9
79-6
82.7
70.3
81.8
80.4
68.5
57.8
83.5
72.5
50.8
86.0
73.6
Table 2: Effectiveness analysis on contrastive-based method and our methods. "BS" and "Mem" represent. the batch size and peak memory on a single GPU. The running time is measured on Tesla A100 GPU with 40 epochs.
DomainNer Net (Rw CII)
Batch Size
Memory
Time
Accuracy
AdaConstrast, Chen al. (2022)
128
>32G
70.2
C-SFDA Karim
2023
256
>64G
70.8
GPL
Litrico et al.
2023
256
>64G
3h
74.2
Ours
128
17G
2h
76.9
Table 3: Accuracy (%) on large-scale DomainNet dataset (ResNet-50). "SF" denotes source-free. We
highlight the best results.
Method
SF
Rw-+I
Rw-Pt
Pt-+II
Cl-Sk
Sk-Ptt
Rw-SK
Pt-
Avg.
ResNet-50 He al.
2016
58.8
57.7
52.6
47.3
73.2
57.4
MCC Jin et al. 2020
X
44.8
65.7
41.9
34.9
47.3
35.3
72.4
48.9
CDAN
Long
65.0
63.7
53.1
63.4
54.5
73.2
62.5
GVB Cui et al.
2020
X
68.2
69.0
63.2
56.6
63.1
62.2
78.3
65.2
MME
Saito et
201
70.0
67.7
69.0
56.3
64.8
61.0
76.0
66.4
TENT Wang et al.
2020
58.5
65.7
57.9
48.5
52.4
54.0
67.0
57.7
G-SFDA Yang 202 la
202 1b
63.4
67.5
62.5
55.3
60.8
75.2
63.3
NRC
Yang et al.
67.5
68.0
67.8
57.6
59.3
58.7
74.3
64.7
SHOT
Liang
2020a
67.7
68.4
66.9
60.1
66.1
59.9
80.8
67.1
Ada Constrast Chen et (022
70.6
69.8
69.3
58.5
66.2
60.2
80.2
67.8
AaD
Yang et al. 2022
70.2
69.8
68.6
58.0
65.9
61.5
80.5
67.8
CaC
Zhang et 2022
70.0
68.8
70.9
66.8
60.3
78.6
68.3
C-SF 13
Karim et
2222
70.8
71.1
68.5
62.1
67.4
62.7
80.4
69.0
GPL Litrico al. 2023
74.2
68.8
67.5
65.7
76.5
69.6
Ours
76.9
71.8
75.4
65.5
69.9
64.6
83.2
72.5
This work ResNet-34 backbone.

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Published in Transactions on Machine Learning Research (06/2025)
Table Accuracy (%) on large-scale VisDA-C dataset (ResNet-101). "SF" denotes source free. We highlight the best results.
Mothod
SF
plane
beyle
hus
CIT
honse
knife
meyle
person
plant
slethrr
train
truck
Avg.
ResNet. 101
He ml al.
20166
X
55.1
53.3
61.9
39.1
80.6
17.9
79.7
31.2
81.0
26.5
73.5
8.5
52.4
MCC
Jin et a.
2020
X
88.7
80.3
80.5
71.5
90.1
93.2
85.0
71.6
89.1 1
73.8
85.0
36.9
78.8
STAIE
La
ml. X720
x
95.0
84.0
84.6
73.0
91.6
91.8
85.9
78.4
94.4
84.7
87.0
42.2
82.7
RWOT
Xu a.
2020
95.1
87.4
85.2
38.6
96.2 2
95.7
90.6
80.0
94.8
90.8
88.4
47.9
84.3
CAN
Kang
2019
87.2
82.5
96.2
90.8
87.5
59.9
87.2
SHOT
Liang et al.
20220a
88.5
80.1
57.3
93.
94.9
80.7
90.5
89.1
86.3
58.2
82.9
DIPE
Wang
2012
95.2
87.6
78.8
35.9
93.9
95.0
84.1
81.7
88,9
85.4
58.0
83.1
HCL
Huang
202T
93..3
85.4
80.7
68.5
91.0
88.1
86.0
78.6
86.6
88.8
80.0
74.7
83.5
Nell Xia el
2021
91.0
87.8
85.6
66.8
93.7
96..1
85.8
81.2
91.6
88.2
86,5
56.0
841.3
GA Yang el all G
20211
96.1
88.3
85.5
74.1
97.1
055.4
89.5
79.4
95.4
92.9
89.1
42.6
85.4
NRC Yang et all
1021
96.8
91.3
82, 1
62.4
96.2
955.9
86.1
80.6
91.8
91..
90.1
39.7
85.9
SFDA-DE Ding et al 22122
95.3
91.2
77.5
72.1
95.7
97.8
85.5
86.1
95.5
93.0
86.3
61.6
$6.5
AdnCont Cleen
2022
97.0
84.7
84.0
77.3
966.7
93.8
91.9
$4.8
94.3
93.1
94-1
49.7
86.88
CoWA
al
89.7
83.9
89.3
92.1
88.7
53.8
86.9
C Zhang
2022
96.6
86.8
86.4
78.4
966.4
96.2
93.6
83.8
96.8
95.1
89.6
50.0
87.3
BDT A uudu el 20122
87.8
C SF DA Kariun el a. 2022
97.6
88.8
86.1
72.2
97.2
94.4
92.1
84.7
93.0
90.7
93.1
63.5
87.8
RECS
Than & Stm
2021
95.3
90.0
85.8
72.7
96.9
97.7
91.5
88.0
95.3
93.6
90.0
56.5
87.8
Ouzs
98.0
88.0
86.4
82.3
97.8
96.2
92.1
85.0
95.5
91.7
93.8
56.2
88.6
4.3
Comparison with State-of saaee-of-the-ate
4.3.1
Quantitative Results
We compare our proposed method against popular source-present and souroe-free methods on three benchmark datasets:
DomainNet, and VisDA-C. We report the Top-1 accuracy, and the results are presented in Table to Table 4 In the Office-Home dataset, as shown in Table [ our proposed method achieves the best performance in terms of Top-1 average accuracy, which comparable to the most recent source-free frree method C-SFDA Additionally, our method in sub-transfer tasks achieves the highost accuracy (soe bold in Table 1 vs. only one sub-transfer lask in C-SFDA. For the DomainNet dataset, demonstrated in Table our proposed method exhibits significant improvements over all baselines. With average Top-1 accuracy of 72.5%, our method outperforms the best source-free baseline by nearly 3% and surpasses the best source-present baseline by 6.1%. Moreover, our method achieves the best performance in almost all domain shifts. On the VisDA-C dataset, presented in Table q our proposed method outperforms the state-of-the-art method C-SFDA Karim et al. (2023) by 0.8%. Furthermore, our method achieves the best performance in specific classes such as "plane", "bus", "car", and "horse". These results clearly demonstrate the superiority of our proposed method across the evaluated datasets, showcasing its effectiveness in source- free domain adaptation scenarios.
4.3.2 Effectiveness Analysis
We conducted an analysis and comparison of the memory usage and running time of our method with recent. works, including AdaContrast Chen et al. (2022). C-SFDA Karim al. (2023). and GPL. Litrico et al. (2023). Interestingly, our method requires normal memory usage, whereas the other methods consune more than 32GB of memory. Despite using standard memory, approach achieves higher accuracy in comparison. Additionally, the running time of our method is considerably less than that of GPL.
4.4 Ablation Studies
4.4.1
Component-wiss Analysis
In this section, we conduct ablation studies to analyze the contribution of each component in our method on three benchmark datasets: Offico-Hone DomainNet, and VisDA-C. The results are summarized in Table 5 in which the HCPR (Hypothesis Consolidation from Prediction Rationale) component makes the most contributions to the promotion of accuracy. Specifically, compared to only using FixMatch, combining
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Table Ablation study of the proposed components calculated by average accuracy (%) on the Office-Home Home (O-H), DomainNeet (DN) and VisDA-C datasets. PA stands for model pre-adaptation (Sec. 3.1 HCPR
Sec. 3.2 stands for hypothesis consolidation from prediction rationale, FM stands for FixMatch (Sec. 3.3
#
PA
HCPR
FM
O-H
DN
VisDA-C
0
x
60.2
55.6
46.6
1
X
64.2
60.6
62.3
2
x
68.6
70.6
85.2
3
V
72.1
67.4
86.2
4
V
72.7
69.6
87.5
5
V
72.2
67.5
86.2
6
V
73.6
72.5
88.6
both FixMatch and HCPR significantly improves accuracy by 4.4%, 10.0%, and 22.9% on the respective datasets. Additionally, in the case of combining both PA (Pre-Adaptation and HCPR, we execute PA again following HCPR to integrate the consolidation outcomes from HCPR. This showcases a substantial enhancement accuracy, with improvements of 0.6%, 2.2%, and 1.3% on the respective datasots compared to solely employing PA. Last but not least, Removing HCPR from the method leads to performance drop of 1.4%, 5%, and 2% points Office-Home, DomainNet, and VisDA-C, respectively.
4.4.2 Impact of Model Pre-adaptation
Table 6:
Comparison of step 1 w/o FAR, w/o step 1, SHOT as step 1, and Ours on the per-class accuracy and average top-1 accuracy on the VisDA-C dataset.
Method
plane
beyle
bus
car
horse
knife
mcyle
person
plant
sktbrd
train
truck
Avg.
step 1 w/o FAR
92.2
78.5
78.3
75.1
90.4
91.3
85.4
78.5
84.8
88.7
86.8
0.0
77.5
w/o step
97.8
83.3
80.8
77.4
95.8
98.1
91.2
82.3
91.8
82.8
92.2
46.5
85.2
SHOT as step 1
97.5
84.6
83.0
74.2
96.5
93.7
92.8
86.7
93.5
92.6
89.7
56.9
86.8
Ours
98.0
88.0
86.4
82.3
97.8
96.2
92.1
85.0
95.5
91.7
93.8
56.2
88.6
To assess the impact of model pre-adaptation, we perform experiments using four different settings on the VisDA-C dataset: model pre-adaptation removing the second term CFAR in Eq. referring to "step w/o FAR"; the proposed method without model pre-adaptation referring to "w/o step 1": Using SHOT's loss as model pre-adaptation
replace Eq. 1, referring to "SHOT as step 1"; and the proposed method with model pre-adaptation using Eq. 1 referring to "Ours". The experimental results can see, we have the following observations: First, compared to "SHOT step 1", the proposed method encouraging smooth prediction has a better average accuracy (86.8% vs. 88.6%), which demonstrates the superiority of making smooth prediction on the data manifold compared to one-hot prediction in Liang ct al. 20200a). Sccond, when removing step 1, referring to "w/o step 1," the average accuracy dropped by 3.4%. This indicates that Eq. is helpful for model pre-adaptation and improves the ability of the model distinguish image classes in the target domain. Third, when removing CFAR in step 1 referring "step w/o FAR", the average performance drop dramatically from 88.6% to 77.5%. This demonstrates that the CPAR plays vital role in keeping class balance and avoiding some missed classes.
shown in Table As
4.4.3 Impact of k the Number of Prediction Hypotheses Per Instance
In our method, we choose labels from the top & highest posterior probabilities as the prediction hypothesis. In this section, we investigate the impact of the value of k. Table 7 shows the accuracy achieved with different. k. From the result, we can see that using hypotheses has already led good performance and choosing 3-6 hypotheses leads to optimal performance.
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Table DomainNet (Pt+CCI) Top-1 accuracy (%) of the proposed method with the different number the prediction hypotheses k. We find yields the optimal results.
k
2
3
4
5
Accuracy
73.7
74.2
75.4
75.3
74.8
Accuracy (%) w.r.t. different
z=3
z=2
2=4
2=5
z=1
74
66
epoch
Figure 4: Accuracy of different z in our method on the DomainNet. (PI-+CI) dataset. When step (0-9 epochs) achieves and maintains the best results, HCPR plays pivotal role in enhancing the performance of the model.
4.4.4 Impact of the = -the Number of Nearest and Furthest Neighbor
Table DomainNet (Pt-+CI) accuracy (%) of the proposed method with diffcrent number of the z.
1
2
3
4
5
Accuracy 73.2
74.7
75.4
74.7
74.3
In the initial step of our model pre-adaptation, we select the z-nearest and 2-furthest neighbors for each target sample. In this analysis, we examine the influence of the 2 value. Figure 4 and Table g showcase the performance throughout the training process and top-l accuracy of classification on the DomainNel (Pt+CI) dataset for different values of z. The results indicate that even with just nearest and furthest neighbor, we achieve favorable classification accuracy, and sclecting 2-5 nearest and furthest neighbors yields optimal performance. Moreover, as observed in Figure | it is worth noting that when step 1 (0-9 epochs) achieves and maintains the best results, HCPR plays a pivotal role in enhancing the performance of the model.
4.4.5 Impact of the Two Ranking Thresholds T1 and T2
To assess the influence of ranking thresholds in our method, examined the percentage values тi and relative to the total number of samples. Specifically, we analyzed their impact the Top-1 average accuracy on the VisDA-C dataset, as illustrated in Figure 5 Our analysis, depicted in Figure 5 revealed that the proposed method exhibits robustness to the specific values of T and 72.
4.4.6 The Benefit of Using Rationale Representations
To further understand the benefit of using the rationale representation from multiple hypotheses, explore alternative method that replaces the proposed second step by using feature centroids rather than rationale centroids. Since the feature is invariant to the prediction hypothesis, only the top predicted class will be considered. More specially, we first generate pseudo-label for each instance and calculate the feature centroid similar our approach. Then we rank instances basod on the Euclidean distances between their features and the corresponding class centroid. The top J features closest the class centroid are assigned reliable pseudo labels, while the remaining samples are left for step 3. We refer to this method as "near-centroid selection".
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3.5
84.2
87.8
3.0
2.5
2.0
S 1.5
8.
68.5
88.6
85.3
1.0
8.0
0.5
66.2
0.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Ti
(%)
Figure VisDA-C average accuracy (%) the proposed method using different тι and T2.
Table 9: Average accuracy (%) of our HCPR centroid collection the Office-Home and DomainNet datasets.
Method
Office-Home
DomainNet
near-centroid selection
72.6
69.6
Ours
73.6
72.5
Accuracy (%) on Office-home (Cl->Pr)
Durs Ours w/ updating step2
80
78
76
74
72
epoch
20
Figure 6: Office-Home accuracy Ours and Ours w/ updating step 2 across varying epochs.
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Accuracy (96) Office-home (Cl->Pr)
stopl+ recursive scap2 step3+ recursive stop2
stepérstephhreeersiie step2
Ours
80
75
70
10
Figure 7: Accuracy of different components in our method with Recursive HCPR on the Office-Homt (CI++Pr) datasct.
Table 9 presents the comparison results on the Office-Home and DomainNel datasets. As seen, while such an approach still leads to improvement over using step 1 and step 3 alone (by refereenfrnnning Table 5), it is still inferior to the use of HCPR. This clearly demonstrates the benefits of the latter.
4.4.7 Investigation of Recursively Applying HCPR
One may wonder if recursively applying HCPR will lead to additional improvement. To this end, we create a variant of our method by alternatively applying step 2 and step 3, hoping that they may mutually enhance each other. We conducted experiments on the Offico-Home (CI-Pr) dataset. The results are depicted in Figure G where the red curve represents our method using the second step only once, i.e. the hypothesis consolidation occurs between model pre-adaptation (0-9 epochs) and semi-supervised learning (10-40 epochs). The blue curve represents our method with the second step updated at the 15th, 20th, and 25th epochs. From the results, we observed that recursively applying HCPR does not lead to an improvement as one may expect.
We also conduct experiments with HCPR applied recursively to only model pre-adaptation or FixMatch. Specifically, we conduct experiments using the Office-Home (CI-Pr) dataset and configure the following scenarios:
Combining Step and Step 2, with Step 2 calculated at the 9th 15th, and 20th epochs (indicated by the green curve in Figure
Combining Step and Step 2, with Step 2 calculated at the 7th, 15th, and 20th epochs (indicated by the ycllow curve Figure
Combining Step and Step 3, with Step 2 calculated at the Oth, 15th, and 20th epochs (indicated by the purple curve in Figure 7).
Our method, is represented by the red curve.
Our observations indicate that utilizing Step 2 only once is sufficient, and the recursive HCPR application does not yield improvements. However, we do note that HCPR plays a crucial role in enhancing FixMatch, particularly in improving the quality of pseudo-labels.
4.5 Pseudo-laber Quantity and Quality
In this section, we assess both the quality and quantity of pseudo-labels generated by each component of our method, comparing them with the source model alone and SHOT, Pseudo-label quantity is measured by the ratio of selected samples to the total samples, while pscudo-label quality is defined as the precision of the selected samples. The results are shown in Table [0] As seen, using the original source model generates good pseudo-label quality within the selected group, but only small number of samples satisfy the high confidence
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Table 10: Comparison of pseudo-label quantity and quality on DomainNet (Rw-+CI). Quantity (%) refers to the proportion of selected samples to total samples. Quality (%) refers to the precision (%) of the chosen sample. "con" represents confidence.
DomainNet (Rw->CI)
Quantity (%)
Quality (%)
source model only (con>>0.95)
3.95
95.80
SHOT (con>0.995)
61.83
80.38
PA only (con>0.95)
79.13
80.76
HCPR only PA+HCPR
21.35
84.02
24.65
90.76
80
100
Clacsfication
70-
95
75
Pheurto-abel Quaminy
60-
90
50-
Quantity
85
Acorscyyte
40-
Pseudo label Quality 04)
80
30-
75
65
20
70
10-
65
55
bpoch
(a) Pscudo-label quantity and quality w.r.t. different
(b) Classification accuracy w.r.t. different epochs.
epochs.
Figure 8: Pseudo-label quantity, quality and classification accuracy of our method over training on DomainNet (R).CII.
condition. On the other hand, SHOT and PA select a large number of samples but with relatively poor quality of approximately 80%. In comparison, PA+HCPR achieves both good pseudo-label quality (90.76%), and a substantial quantity of pseudo-labels (24.65%). When comparing HCPR only and PA only, we observed that PA generates nearly four times as many pseudo-labels as HCPR but with lower quality. This suggests the presence of significant. noise in the pseudo-labels generated by PA.
The training progress to both the quantity and quality of pseudo-labels can be shown in Figure Our findings revealed that in the initial step with PA (0-9th epochs), there is a significant increase in the quantity of pseudo labels, albeit accompanied by gradual decrease in their quality. However, with the assistance of HCPR (after 9th epch, before 10th epoch), the quality of pscudo-labels experiences a significant increase, accompanied by a substantial quantity. the subsequent third step involving FM (10-40th epochs), the quality of pseudo labels has gradual improvement, which subsequently stabilizes at a consistent level.
4.6
Incorporating the Proposed Method into Existing Approaches
4.6.1
Prediction-level alignment
The proposed method can be seamlessly integrated into existing network architectures, such as SHOT Liang et al. (2020a) and AaD Yang al. (002)). Specifically, we replace the pre-adaptation phase in our first step with SHOT and AaD, resulting in the combined approach referred to as "SHOT+Ours and " "AaD+Ours", The integration process unsupervised ncarest class centroid approach and AaD's feature clustering and cluster assignment approach. Then, to refine these pseudo labels and address polential noise and inaccuracies, we utilize hypothesis consolidation of prediction rationale. The refined pseudo-label set is used as the labeled dataset, while the remaining samples are treated as unlabeled. Consequently, the SFUDA problem transformed into a semi-supervised learning problem. The experimental results, as shown in Table [I] demonstrate the superiority of the proposed method integrated into the SHOT and AaD objectives. Across the Office-Home (Avg. 1.6%
be summarized as follows: first, pseudo labels are generated using SHOT's
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Table 11: Accuracy (%) our method combined with existing SHOT and AaD methods on the Office- Office-Home, VisDA-C and DomainNet datasets.
Method
Ar-CI
Ar-PPr
Ar-Iiw
Cl++Ar
CI-PPr
Cl-iItr
Pr-tAr
Pr-+Cl
Pr-+RW
Iww+AAr
Rw-+CI
Rw-+Pr
Ang.
SHOT Tdang, al. 2020
66.9
78.1
81.0
67.9
78.4
78.1
67.0
54.6
81.8
13.4
58.1
84.5
71.6
+Ours
58.7
79.5
82.1
69.6
80.7
80.0
69.1
56.9
82.3
74.5
59.2
85.3
T3.2
AnD Yang et
82.1
79.8
83.1
72.1
58.5
AnD Oliss
59.8
79.4
82.7
70.0
81.6
80.0
68.5
37.8
83.2
72.7
59.4
86.1
73.4
Method
plane
beyle
bus
car
horse
knife
meyle
person
plant
sktbrd
train
truck
Avg.
SHOT Tlang et al.
2020a
94.3
88.5
80.1
57.3
93.1
94.9
80.7
80.3
90.5
89.1
86.3
58.2
82.9
SHOT Ours
97.5
84.6
83.0
74.2
96.5
93.7
92.8
86.7
93.5
92.6
89.7
56.9
86.8
AsD Yang et al.
2022
97.4
90.5
80.8
76.2
97.3
96.1
89.8
82.9
95.5
93.0
92.0
64.00
88.0
FOur
97.8
87.6
86.7
83.4
97.7
95.4
94.2
83.8
94.6
91.2
92.8
55.6
88.4
Method
Rw +C1
Wt
Pt-+C
CI Cl-SSk
Sk- Pt.
Rw- SSk
Pt
Rw
Avg.
SHOT
Liang et al.
(2020a)
67.7
68.4
66.9
60.1
66.1
59.9
80.8
67.1
SHOT Ours
70.5
70.6
72.5
63.6
68.0
61.1
82.8
69.9
AaD
Yang et al. (2022
70.6
69.8
69.3
58.5
66.2
60.2
80.2
67.8
AaD Ours
75.4
71.3
75.2
64.2
68.4
63.3
82.8
71.5
and t 0.7%), VisDA-C (Avg. 3.9% and 0.4%), and DomainNet-126 (Avg. t 2.8% and f 3.7%) datasets, the integrated approach consistently outperforms the baseline of SHOT and AaD. This indicates that our method complements existing SFUDA baselines and consistently improves their performance by incorporating our approach as a replacement for the model pro-adaptation phase.
Our proposed model pre-adaptation strategy is specifically designed to encourage smooth predictions by aligning similar samples and separating dissimilar ones, which differs from prior methods, such as SHOT, basedd based objectives. This smoothness constraint helps the model adapt more gradually to the larget domain distribution and mitigates overconfident early pseudo-labels.
that rely onc-hot predictions or clustering-
4.6.2 Embedding-leve alignment
Table 12: Accuracy (%) on the VisDA-C dataset comparing our method with Local Aggregation (LA) Zhuang ct al. (219 used as the pro-adaptation step.
Method
plane
beyle
bus
car
knife
meyle
person
plant
sktbed
train
truck
Avg.
Local Aggregation Zimang
2019 step
97.5
84.5
77.4
97.6
94.1
84.0
96.4
92.9
93.4
55.6
88.0
Our
98.0
$8.0
86.4
82.3
97.8
96.2
92.1
85.0
95.5
91.7
93.8
56.2
88.6
This section further investigates the relationship to embedding-level alignment methods, particularly the Local Aggregation (LA) loss Zhuang al. (2019). LA focuses on optimizing the embedding space, whercas our method guides the model's prediction behavior. This distinction is particularly relevant for source-free domain adapiation, where prediction stability directly influences pseudo-label quality. To empirically validate this comnection, replaced our pre-adaptation objective with LA and evaluated the performance the VisDA-C datasct. As shown in Table 12] the LA-based pre-adaptation achieves average accuracy of 88.0%, which is competitive but still slightly lower than our full met hod's 88.6%. Moreover, our approach outperforms LA on most individual classes.
4.7 Visualization
In (-SNE visualization, we compare the results with the state before adaplation by examining three approaches: source model only, AaD Yang et al. (2022), and our method shown Figure g The source model only demonstrates shortcomings, experiencing false predictions within cach class and struggling to establish clear intra-class boundaries. While AaD generally achieves accurate predictions within each class, falls short in generating clear intra-class boundaries. In contrast, our method excels in achieving accurate predictions
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(a) Source model only
(b) AaD
(c) Ours
Figure 9: t-SNE Visualization after 40 training epochs on target features for a randomly selected subset of 6 DomainNet (Rw-+II) Classes. Comparison of results with two baselines: Source Model Only and A&D Yang et al. 2022). Each color in the graphs represents a class of samples. is evident from the visualizations that the proposed method surpasses both 'Source model only' and "AaD' in terms of qualitative performance. This superiority is demonstrated through the generation of intrinsic local consistency and clear intra-class boundaries.
within each class and successfully generates distinct intra-class boundaries, which showcases its ability enhance prediction accuracy and produce well-defined intra-class boundaries.
5 Limitation and Future Work
The current approach relies on having access the entire target training set perform crucial steps like pre-adaptation and identifying the reliable pseudo-labeled set. However, in real-world applications, online adaptation is often more desirable as it doesn't 't require holding large number of target examples. As part of our future work, aim to extend the key idea of this rescarch to the online streaming setting. By doing we can develop a methodology that adapts in real-time to incoming data, allowing for more efficient and effective adaptation in dynamic environments. This extension will enhance the applicability and practicality of the proposed approach in various domains.
Additionally, our method involves several hyperparameters (e.g., the number of neighbors, hypotheses per instance, ranking thresholds, etc.), which may raise concerns about robustness in the absence of labeled target data. While we follow the standard practice in the SFUDA community by selecting hyperparameters using unlabeled input data, we acknowledge that this practice is not ideal in practical deployment scenarios where labeled target data is unavailable.
address this limitation, we now include a discussion of alternative unsupervised hyperparameter selection techniques. Motivated by recent progress in unsupervised model selection, we additionally explored the applicability of Transfer Score Yang al. (2023) to tune the critical hyperparameter & on the DomainNet (Pt+CI). Specifically, from unlabeled largel data. The results revealed that k=4 yielded the highest Transfer Score (1.64) and corresponded to the best performance on the target test set. In contrast, suboptimal values of led to both lower transfer scores and degraded model accuracy. This suggests that Transfer Score may offer promising direction for unsupervised hyperparameter selection in our setting, warranting further exploration in future work.
evaluated multiple candidate values of k using the Transfer Score computed
6 Conclusion
In conclusion, this paper introduces novel approach for Source-Free Free Unsupervised Domain Adaptation (SFUDA), where model needs to adapt to domain without access to target domain labels source
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domain data. By considering multiple prediction hypotheses and analyzing their rationales, the proposed method identifies the most likely correct hypotheses, which are then used as pseudo-labeled data for a semi-supervised learning procedure. The three-step adaptation process, including model pre-adaptation, hypothesis consolidation, and semi-supervised learning, ensures optimal performance. Experimental results remonstrate that the proposed approach achieves stale-of-the-arl performance in the SFUDA task and can be seamlessly integrated into existing methods to enhance their performance.
References
Dian Chen, Dequan Wang, Trevor Darrell, and Sayna Ebrahimi. Contrastive test-time adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 295-305, 2022.
Shuhao Cui, Shuhui Wang, Junbao Zhuo, Chi Su, Qingming Huang, and Qi Tian. Gradually vanishing bridge for adversarial domain adaptation. In Proceedings of the IEEE/CVE conference on computer vision and poltern recognition, pp. 12455 124644 2020.
Shuyang Dai, Yu Cheng, Yizhe Zhang, Zhe Gan, Jingjing Liu, and Lawrence Carin. Contrastively smoothed class alignment for unsupervised domain adaptation. In Proceedings of the Asian Conference on Computer Vision, 2020.
Ning Ding, Yixing Xu, Yehui Tang, Chao Xu, Yunhe Wang, and Dacheng Tao. Source-free domain adaptation via distribution estimation. In Proceedings of the (EE//CVP Conference on Compuler Vision and Pattern Recognition, pp. 2212-7222, 2022.
Yuqi Fang, Tew-Thian Yap, Weili Lin, Hongtu Zhu, and Mingxia Liu. Source-free unsupervised domain adaptation: A survey. Neural Networks, pp. 106230, 2024.
Hao Feng, Minghao Chen, Jimming Hu, Dong Shen, Haifeng Liu, and Deng Cai. Complementary pseudo labels for unsupervised domain adaptation person re-identification. IEEE Transactions Image Processing, 30:2898 2907, 2021.
Yaroslav Ganin and Victor Lempitsky. Unsupervised domain adaptation by backpropagation. In International conference on machine learning, pp. 1180 1189. PMLR, 2015.
Xiang Gu, Jian Sun, and 'oonbeen Xu. Spherical space domain adaptation with robust pseudo-label loss. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pallern Recogriition, pp. 9101 9110, 2020.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. -707778,7 2016.
Judy Hoffman, Fric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Tsola, Kate Saenko, Alexei Efros, and Trevor Darrell. Cycada: Cycle-consistent adversarial domain adaptation. In International conference on machine learning, pp. 1989-1998. Pmlr, 2018.
Lanqing Hu, Meina Kan, Shiguang Shan, and Xilin Chen. Unsupervised domain adaptation with hierarchical gradient synchronization. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pp. 0043-4052, 2020.
Jiaxing Huang, Dayan Guan, Aoran Xiao, and Shijian Lu. Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data. Advances in Neural Information Processing Systems, 34:3635-3649, 2021.
Sergey Toffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pp. 448-456. pmlr, 2015.
Ying Jin, Ximei Wang, Mingsheng Long, and Jianmin Wang. Minimum class confusion for versatile domain adaptation. In Computer Vision-ECC ECCV 2020: 16th European Conference, Glasgou, UK, August 23-28, 2020, Proceedings, Part XXT 16, pp. 464-46 664- Springer, 2020.
17

---

Published in Transactions on Machine Learning Research (06/2025)
Mengmeng Jing, Xiantong Zhen, Jingjing Li, and Cees Snock. Variational model perturbation for source-free domain adaptation. Advances in Newral Information Processing Systems, 77-1788777 2022.
Guoliang Kang, Lu Jiang, Yi Yang, and Alexander G Hauptmann. Contrastive adaptation network for unsupervised domain adaptation. In Proceedings of the IEEE/CVE conference on computer vision and pattern recognition, pp. 4893-4900 2019.
Nazmul Karim, Niluthpol Chowdhury Mithun, Abhinav Rajvanshi, Han-pang Chiu, Supun Samarasckera, and Nazanin Rahnavard. C-sfda: A curriculum learning aided self-training framework for efficient source free domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pollern Recognition, pp. 24120-24131, 2023.
Jogendra Nath Kundu, Akshay R Kulkarni, Suvaansh Bhambri, Deepesh Mchta, Shreyas Anand Kulkarmi, Varun Jampani, and Venkatesh Babu Radhakrishnan. Balancing discriminability and transferability for source-free domain adaptation. In International Conference on Machine Learning, pp. 11710 11728. PMLR, 2022.
Jonghyun Lec, Dahuin Jung, Junho Yim, and Sungroh Yoon. Confidence score for source-free unsupervised domain adaptation. In International Conference on Machine Learning, pp. 12365-12377 12377. PMLR, 2022.
Jingjing Li, Zhiqi Yu, Zhekai Du, Lei Zhu, and Heng Tao Shen. A comprehensive survey on source-free domain adaptation. IEEE Trunsactions on Pallern Analysis and Machine Intelligence, 2024.
Jian Liang, Dapeng Hu, and Jiashi Feng. Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation. In International Conference Machine Learning, pp. 6028-6039. PMLR, 2020a.
Jian Liang, Dapeng Hu, Yunbo Wang, Ran He, and Jiashi Feng. Source data-absent, unsupervised domain adaptation through hypothesis transfer and labeling transfer. IEEE Transactions on Pottern Analysis and Machine Intelligence, 40011):8002-8617, 2021.
Justin Liang, Namdar Homayounfar, Wei-Chiu Ma, Yuwen Xiong, Rui Hu, and Raquel Urtasun. Polytransform: Deep polygon transformer for instance segmentation. In Proceedings of the IEEE/CVF conference computer vision and pallern recognition, pp. 9131-9140, 2020b.
Mattia Litrico, Alessio Del Bue, and Pietro Morerio. Guiding pseudo-labels with uncertainty estimation for source-free unsupervised domain adaptation. In Proceedings of the IEEE/CVE Conference Computer Vision and Pattern Recognition, pp. 7640-7650, 2023.
Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton van den Hengel, Kun Zhang, and Javen Qinfeng Shi. Identifying weight-variant latent causal models. arXiv preprint ar ariiv:220.. 111.2228..111.. 14153, 2022.
Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwci Huang, Anton van den Hengel, Kun Zhang, and Javen Qinfeng Shi. Identifiable latent neural causal models. arXiv preprint 2rXiv2440..15111 2024a.
Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwci Huang, Anton van den Hengel, Kun Zhang, and Javen Qinfeng Shi. Identifiable latent polynomial causal models through the lens of change. In The Twelfth International Conference on Learning Representations, 2024b.
Yuhang Liu, Zhen Zhang, Dong Gong, Biwei Huang, Mingming Gong, Anton van den Hengel, Kun Zhang, and Javen Qinfeng Shi. Revealing multimodal contrastive representation learning through latent partial causal models. arxiv preprint ar Xiv:24000 06223, 2024c.
Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton van den Hengel, Kun Zhang, and Javen Qinfeng Shi. Latent covariate shift: Unlocking partial identifiability for multi-source domain adaptation. Transactions Machine Learning Research, 2025.
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan. Learning transferable features with deep adaptation networks. Tn International conference on machine learning, pp. 97-105. PMT.R 2015.
18

---

Published in Transactions on Machine Learning Research (06/2025)
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan. Conditional adversarial domain adaptation. Advances in neural information processing systems, 31, 2018.
Zhihe Lu, Yongxin Yang, Xiatian Zhu, Cong Liu, Yi-Zhe Song, and Tao Xiang. Stochastic classifiers for unsupervised domain adaptation. In Proceedings of the IEEE/CVE Conference on Computer Vision and Pollern Recognilion, pp. 9111-99111111111111111 9120, 2020.
Ke Mei, Chuang Zhu, Jiaqi Zou, and Shanghang Zhang- Instance adaptive self-training for unsupervised domain adaptation. In Computer Seion-EECV 2020: 16th European Conference, Glasgow, UK, August 23 28, 2020, Proceedings, Part XXVI 16, pp. 415 430. Springer, 2020.
Jaemin Na, Heechul Jung, Hyung Jin Chang, and Wonjun Hwang. Fixbi: Bridging domain spaces for unsupervised domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pottern Recognition, pp. 1094 1103, 2021.
Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, and Kate Saenko. Visda: The visual domain adaptation challenge. arXiv preprint urXiv:1710.0699 2017.
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang. Moment matching for multi-source domain adaptation. In Proceedings of the IEEE/CVF international conference computer vision, pp. 1406 1415, 2019.
Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Trevor Darrell, and Kate Saenko. Semi-supervised domain adaptation via minimax entropy. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 8050 8058, 2019.
Tim Salimans and Durk P Kingma. Weight normalization: A simple reparameterization to accelerate training deep neural networks. Advances in neural information processing systems, 29, 2016.
Ramprasaath R Sclvaraju, Michael Cogswell, Abhishck Das, Ramakrishna Vodantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, pp. 618 626, 2017.
Yangyang Shu, Baosheng Yu, Haiming Xu, and Lingqiao Liu. Improving fine-grained visual recognition low data regimes via self-boosting attention mechanism. In Computer Vision ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23 3-27,, 2022, Proceedings, Part XXV, pp. 469. Springer, 2022.
Yangyang Shu, Anton van den Hengel, and Lingqiao Tiu. Learning common rationale to improve self- supervised representation for fine-grained visual recognition problems. In Proceedings of the IEEE/CVE Conference on Computer Vision and Pottern Recognition, pp. 11392-11401, 2023.
Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li. Fixmatch: Simplifying semi-supervised learning with consistency and confidence. Advances in neural information processing systems, 33:596-608, 2020.
Hui Tang, Ke Chen, and Kui Jia. Unsupervised domain adaplation via structurally regularized deep clustering, In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 8725 8735, 2020.
Qing Tian and Canyu Sun. Rethinking confidence scores for source-free unsupervised domain adaptation. Neural Computing and Applications, pp. 1-16, 2024.
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Sacnko, and Trevor Darrell. Deep domain confusion: Maximizing for domain invariance. arXiv preprint arxiv:1412.3377 2014.
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell. Adversarial discriminative domain adaptation.
In Proceedings of the IEFE conference on computer vision and poltern recognition, pp. 7167-7176, 2017.
19

---

Published in Transactions on Machine Learning Research (06/2025)
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan. Deep hashing network for unsupervised domain adaptation. In Proceedings of the IEEF conference on computer vision and pottern recognition, pp. 5018 5027, 2017.
Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Matthieu Cord, and Patrick Pérez. Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation. In Proceedings of the IEEE/CVF Conference on Compuler Vision and Pollern Recognition, pp. 2526, 2019.
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell. Tent: Fully test-time adaptation by entropy minimization. ar preprinl ar xxX::2006.10720 2020.
Fan Wang, Zhongyi Han, Yongshun Gong, and Yilong Yin. Exploring domain-invariant parameters for source free domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7151-7160, 2022.
Haifeng Xia, Handong Zhao, and Zhengming Ding. Adaptive adversarial network for source-free free domain adaptation. In Proceedings of the IEEE/CVP international conference on compuler vision, pp. 9010 9019, 2021.
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoe V Le. Self-training with noisy student improves imagenet classification. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10687 10698, 2020.
Renjun Xu, Pelen Liu, Liyan Wang, Chao Chen, and Jindong Wang. Reliable weighted optimal transport for unsupervised domain adaptation. In Proceedings of the IEEE/CVE ECFF conference computer vision and pottern recognition, pp. 4394-4403, 2020.
Jianfei Yang, Hanjie Qian, Yuecong Xu, Kai Wang, and Lihua Xie. Can we evaluate domain adaptation models without target-domain labels? arXiv preprint ar Xrx:22330 18712, 2023.
Shiqi Yang, Joost van de Weijer, Luis Herranz, Shangling Jui, et al. Exploiting the intrinsic neighborhood structure for source-free domain adaptation. Advances in neural information processing systems, 34: 19393-29409 2021a.
Shiqi Yang, Yaxing Wang, Joost Van De Weijer, Luis Herranz, and Shangling Jui. Generalized source-free domain adaptation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8978 8987, 2021b.
Shiqi Yang, Yaxing Wang, Kai Wang, Shangling Jui, et al. Autracting and dispersing: A simple approach for fource-free domain adaptation. In Advances in Neural Information Processing Systems, 2022.
Fei Yu, Mo Zhang, Hexin Dong, Sheng Hu, Bin Dong, and Li Zhang. Dast: Unsupervised domain adaptation in semantic segmentation based discriminator attention and self-training. In Proceedings of the AAAT Conference on Arlificial Intelligence, volume 35, pp. 10754 10762, 2021.
Ningyuan Zhang, Jie Lu, Keqiuyin Li, Zhen Fang, and Guangquan Zhang. Sourco-free unsupervisod domain adaplation: Current research and future directions. Neurocompuling, 564:126921, 2024.
Ziyi Zhang, Weikai Chen, Hui Cheng, Zhen Li, Siyuan Li, Liang Lin, and Guanbin Li. Divide and contrast: Source-free domain adaptation via adaptive contrastive learning. Advonces in Neural Information Processing Systems, 35:5137-5149, 2022.
Chengxu Zhuang, Alex Lin Zhai, and Daniel Yamins. Local aggregation for unsupervised learning of visual embeddings. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 6002 6012, 2019.
Yang Zou, Zhiding Yu, BVK Kumar, and Jinsong Wang. Unsupervised domain adaptation for semantic segnentation via class- balanced self-training. In Proceedings of the European conference on computer vision (ECCV), pp. 999-305,50 2018
20


Reviews:
[{"review_id": "NRtk6CdTNT", "reviewer": "Reviewer_fgZL", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "### Strengths:\n\nThe overall concept is clear and makes good use of existing tools.\n\nThere are a number of ablation studies to identify key components of the model.\n\nThere are many comparisons to prior models in the literature.\n\n### Weaknesses:\n\nGiven the variability in performance, it's unclear whether there is statistically signifcantly improved performance.  The practical impact of these results needs to be more fully discussed.\n\nDetails on the full evaluation procedure are missing.  How was the data split?  Is performance reported on a held-out test set or simply from the validation results?\n\nIt is unclear how applicable or robust the proposed approach would be in practice.  As is highlighted in the ablation studies and robustness analysis, performance is highly variable across model settings.  As in practice we don't have labels in the test set, it is critical to discuss strategies for selecting such values in real-world settings, and evaluating the robustness compared to prior work.  This in particular needs to be added to the limitations.\n\nSection 4.6 requires additional discussion as to why there is different performance from these methods compared to the proposed model pre-adaptation.\n\n\n#### Minor:\nI think there might be a typo in Equation 4, where the max is not clearly defined and the indicator function seems to be over the entire second term. Please check that.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This manuscript proposes a new approach for source-free unsupervised domain adaptation that is based on first pre-training the model to smooth predictions (model pre-adaptation), constructing hypotheses on the target data to identify high-quality pseudo-labels (hypothesis consolidation), and fine-tuning by using the pseudo-labels in a semi-supervised framework.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "In the abstract and introduction, it is repeatedly stated that you are using semi-supervised learning, but I misinterpreted what you meant by it at first.  I would argue that you're using a framework designed for semi-supervised learning, but using pseudo-labels instead.  I would clarify the description to make it clear that you're using pseudo-labels in the semi-supervised learning.\n\nUncertainties need to be added to the results table, along with statistical evaluation.  Performance improvements need to be more rigorously discussed on their potential impact and the statistical evidence for them.\n\nFull details on model training, selection, and data splitting need to be added.\n\nThe definition of the distribution $p(x)$ used in equation 1 needs to be added, along with details on how KL is calculated.\n\nDetails on how model selection in the real world would be performed needs to be added, along with a greater discussion on the robustness of the technique.  The discussion of this and the practical implementation of model selection should be discussed further in the limitations.", "broader_impact_concerns": "None.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "w0y10jQZoa", "reviewer": "Reviewer_sb4d", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strength:\n\n- The challenge of SFUDA has been of greated research interest with its various applications, such as adapting a known model to more specific yet label-intensive domains. Therefore, the topic discussed in this paper is of significance to the research community.\n- The idea of leveraging GradCAM, which showcase the rationale of prediction (and which have been used commonly since its introduction for visualization of model capabilities), for pseudo-label refinement instead of a simple prediction probability is actually very interesting and insightful. It especially holds true when probabilities may be inaccurate and misleading especially when performing SFUDA across large domain gaps.\n- The results presented in the paper is decent. The authors achieve good overall performances in the image-based DA benchmarks evaluated.\n- The authors have also show good effort in understanding each component and possible impact elements of the proposed method by a series of comprehensive ablation studies.\n\nWeaknesses:\n\nDespite the insightful idea and overall good performance, the motivation and clarity of the proposed method is not clear. There are also some lack of details and analysis in the presented results, details as follows:\n- The motivation and reasoning for the pre-adaptation phase is just too vague. While the authors try to prove it empirically, there should be some hypothesis and rationale that first leads to the idea of the inclusion of this phase, which is uncommon in previous SFUDA methods. The words \"empirical findings\" cannot does not suggest a valid rationale behind the introduction of the pre-adaptation phase.\n- Continue with the pre-adaptation phase, the authors only showcase the effect of the pre-adaptation phase on ONE specific class (i.e., truck). Is this class presented after careful selection or a random selection?\n- One concern that may be raised regarding the use of GradCAM for pseudo-label refinement based on ranking is that the ranking operation is not differentiable. In this case, would this lead to a gradient loss and difficulties in training?\n- A question related to the semi-supervised learning phase is whether this FixMatch is just leveraged out-of-the-box or could it be further adapted given the additional information obtained from previous phases. The current strategy seems very intuitive and there seems to be a waste of information.\n- With regards to the results presented, it is observed that the proposed method may not reach the optimal performance in various scenarios in VisDA-C, what is the possible reasons?\n- Further, it is also observed that the authors have not presented the running time (which I believe should be the training time) for AdaContrast and C-SFDA, any particular reaons to the lack of data?\n\nOverall, the paper is interesting, but more clarifications is definitely required for improvement.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper addresses Source-Free Unsupervised Domain Adaptation (SFUDA), where a model must adapt to a new target domain without access to labeled target data or source domain data. The authors propose a novel three-step method: which includes a model pre-adaptation phase to encourage smooth predictions for domain gap reduction; hypothesis consolidation from prediction rationale through GradCam for more refined pseudo-labels; and lastly semi-supervised learning with FixMatch. The method achieves good performance on several benchmarks.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "The most critical requested changes are:\n- Clarification on the rational for the model pre-adaptation phase.\n- Detailed results that showcase the effects of the pre-adaptation phase on at least one dataset but across all classes.\n\nPlease also see the comments and questions above for other changes that may be beneficial to the paper.", "broader_impact_concerns": "Not applicable", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "DAUcK9WgS7", "reviewer": "Reviewer_oLK1", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths**\n- Source-free adaptation is practical in various realistic scenarios where both source privacy and distribution shifts are the concerns.\n- The proposed approach is comprehensive in terms of adapting the source-trained model towards the target domain, including three steps at different stages. In addition, the approach is also intuitive and easy to understand.\n- The prediction rationale used in the second step is new for SFUDA, although it may be popular in fine-grained recognition tasks.\n- Experiments on three popular SFUDA benchmarks show that the proposed approach achieves state-of-the-art performance.\n\n**Weaknesses**\n- The main weakness is that many hyperparameters are involved and it is unclear how to determine the suitable values for them with only unlabeled target data. The hyperparameters include the number of nearest/furthest neighbors per instance z, the hypothesis per instance k, two ranking thresholds, the model pre-adaptation epochs, and so on. \n\n- Since the approach highly relies on high-quality pseudo-labeling or high-quality initial target predictions, it would be necessary to study the adaptation tasks where the initial target predictions are of low quality. Especially in realistic scenarios, no assumptions can be made for the testing data, initial target predictions may well be inaccurate.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This submission proposes a new approach to solving the source-free unsupervised domain adaptation (SFUDA) problem. The framework consists of three steps: model pre-adaptation via encouraging smooth predictions, hypothesis consolidation from prediction rationale, and semi-supervised learning via consistency regularization and pseudo-labeling. Results on three SFUDA benchmarks demonstrate the effectiveness of the proposed framework and the ablation studies also support the effectiveness of each step.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "In addition to address the above two weaknesses, I have the following comment:\n\nThe pre-adaptation loss seems to share similarities with the local aggregation loss [1] where the near neighborhood is encouraged and the farther neighborhood is discouraged. Necessary discussions may benefit the presentation of step 1.\n\n[1] Local Aggregation for Unsupervised Learning of Visual Embeddings. ICCV 2019", "broader_impact_concerns": "None. The authors have discussed the potential broader impacts.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)