Diffusion-Driven Progressive Target Manipulation for Source-Free Domain Adaptation
Authors: Yuyang Huang
Yabo Chen
Junyu Zhou
Wenrui Dai
XIAOPENG ZHANG
Junni Zou
Hongkai Xiong
Qi Tian
Venue: NeurIPS 2025 poster ()
Keywords: Source-free Domain Adaptation
Diffusion Models
Controllable Generation
Primary area: deep_learning

Abstract: Source-free domain adaptation (SFDA) is a challenging task that tackles domain shifts using only a pre-trained source model and unlabeled target data. Existing SFDA methods are restricted by the fundamental limitation of source-target domain discrepancy. Non-generation SFDA methods suffer from unreliable pseudo-labels in challenging scenarios with large domain discrepancies, while generation-based SFDA methods are evidently degraded due to enlarged domain discrepancies in creating pseudo-source data. To address this limitation, we propose a novel generation-based framework named Diffusion-Driven Progressive Target Manipulation (DPTM) that leverages unlabeled target data as references to reliably generate and progressively refine a pseudo-target domain for SFDA. Specifically, we divide the target samples into a trust set and a non-trust set based on the reliability of pseudo-labels to sufficiently and reliably exploit their information. For samples from the non-trust set, we develop a manipulation strategy to semantically transform them into the newly assigned categories, while simultaneously maintaining them in the target distribution via a latent diffusion model. Furthermore, we design a progressive refinement mechanism that progressively reduces the domain discrepancy between the pseudo-target domain and the real target domain via iterative refinement. Experimental results demonstrate that DPTM outperforms existing methods by a large margin and achieves state-of-the-art performance on four prevailing SFDA benchmark datasets with different scales. Remarkably, DPTM can significantly enhance the performance by up to 18.6\% in scenarios with large source-target gaps.

Diffusion-Driven Progressive Target Manipulation for Source-Free Domain Adaptation
Yuyang Huang'; Yabo Chen'; Junyu Zhou', Wenrui Dai Xiaopeng Zhang";
Junni Zou't Hongkai Xiong', Qi Tian
SShanghai Jiao Tong University, Shanghai, China Huaaei Inc., Shenzhen, China
(huangyuyang, chenyabo, blabla, daiwenrui, zoujunni, xionghongkai)@sjj edu cn zxphistory@gmail com, tian. .q gii@huawei... com
*These authors contributed equally to this work. Corresponding authors: Wenrui Dai; Xiaopeng Zhang; Junni Zou.
Abstract
Source-free domain adaptation (SFDA) a challenging task that tackles domain shifts using only pre-trained source model and unlabeled target data. Existing SFDA methods are restricted by the fundamental limitation of source-target domain discrepancy. Non- Non-generation SFDA methods suffer from unreliable lebulo-labels in challenging scenarios with large domain discrepancies, while generation baased SFDA methods are evidently degraded due to enlarged domain discrepancies in creating pseudo-source data. To address this limitation, we propose a novel generation-based framework named Diffusion-Drivee Driven Progressive Target Manipula- tion (DPTM) that leverages unlabeled target data as references to reliably generate and progressively refine a pseudo-target domain for SFDA. Specifically, the target samples into a trust set and a non-trust set based on the reliability of pseudo-labels to sufficiently and reliably exploit their information. For samples from the non-trust set, we develop manipulation strategy to semantically trans- form them into the newly assigned categories, while simultancously maintaining them in the target distribution via latent diffusion model. Furthermore, we design a progressive refinement mechanism that progressively reduces the domain dis- crepancy between the pseudo-target domain and the real target domain via iterative refinement. Experimental results demonstrate that DPTM outperforms existing methods by large margin and achieves state-of-the-ar performance four pre- vailing SFDA benchmark datasets with different scales. Remarkably, DPTM can significantly enhance the performance by up to 18.6% in scenarios with large source-target gaps.
divide
Introduction
Deep learning has achieved remarkable success under the independent and identically distributed (i.i.d.) assumption. However, it suffers from significantly degraded performance on out-of-distribution (OOD) data due domain shifts. Unsupervised domain adaptation (UDA) mitigates this issue by aligning feature distributions between labeled source and unlabeled target domains, but has to access Source-free domain adaptation (SFDA) considers more practical but challenging scenario where only the pre-trained source model and unlabeled target data are available 19 and precludes access to source samples during adaptation.
both datasets during adaptation 42 4
Existing SFDA methods can be primarily classified into non- generateerionnn and generation aancet methods, with both exhibiting inherent limitations on practical effectiveness. Non-generation methods 23 57 38 37 predominantly rely on pseudo-labels generated by the source model, and categorize them into small subset of reliable labels and predominant subset of unreliable pseudo-labels
61 35
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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5 58 The unreliable beeudo-labels contain substantial label noise, and cannot be easily exploited extract useful information or infer correct labels through refinement processes. Unfortunately, the amount of label noise is inherently determined by the degree of domain shift between source and target domains [53] 24 This fundamental limitation severely compromises the effectiveness of non-genee -eneeearttiion methods in challenging scenarios with large domain gaps, and results in significantly unstable performance across different adaptation tasks. For instance, empirical results (37] show that, when deploying the same source model across different target domains, significant performance discrepancies emerge (e.g,, accuracy over 90% for Ar-CI vs. about 60% for Ar-Pr in the Office-Home dataset). These results underscore the critical sensitivity of non con-generation methods to domain shift.
based on certainty metrics [21 43 8 48
51
Generation based SFDA methods primarily operate at the data level B1 6 26 9
Although these
methods could theoretically circumvent the limitation of con-generation methods by avoiding directly using unreliable pseudo-labels, most of them still fail to escape from this domain shift limitation due to the problematic paradigm that generates a pseudo-source domain to convert the SFDA task into conventional UDA task. The restriction caused by the domain shift between the pseudo-source and target domains is not addressed. Moreover, the generation process often incorporates irrelevant domain features that could further enlarge the discrepancy between the source and target domains for the pseudo-source domain. Consequently, these methods suffer from unsatisfactory performance due to domain shifts.
In this paper, we reveal that existing SFDA methods are fundamentally limited by source-targe domain shifts. To break this bottleneck, we resort to novel generation-based paradigm that directly generates the pseudo-target domain. domain... We propose a Diffusion--Driven Progressive Target Manipulation (DPTM) framework that reliably generates and progressively refines the pseudo target domain to reduce the domain discrepancy from the real target domain and address the fundamental limitation on domain shift for existing methods. The proposed method is shown to achieve remarkable performance gains in challenging DA scenarios with large source-target domain shifts.
To sufficiently and reliably exploit the pseudo-label information, we partition the target data into a trust set and a non-trust set based on the prediction uncertainty of the target model initialized using the source model. For the trust set with low uncertainty. we directly adopt pseudo-labels as supervisory signals for training the target model, following prior works that have established their reliability [21 the target distribution contained in the potentially unreliable samples from the non-trust set. We uniformly assign new category label to each of them to prevent potential class imbalance and employ a manipulation strategy to semantically transform each sample toward its newly assigned label while preserving its target-domain features with latent diffusion model (2). The manipulated samples simultaneously turn their assigned labels into useful supervisory signals and keep aligned with the target distribution to enhance the adaptation of target models. Furthermore, we propose Progressive Refinement Mechanism that iteratively refines the pscudo-target domain as well as the target model to progressively reduce the residual label noise due imperfect pscudo-labeling and the accumulated domain discrepancy caused by the manipulated non-trust set. This significantly diminishes the quantity of non-trust samples and thereby mitigates overall domain shift.
48
51 5. 58
Moreover, we also exploit the rich information about
To be concrete, the proposed manipulation strategy of non-trust samples consists of three components. Firstly, as the sampling starting point of diffusion models has been proven to significantly influence the generated image [45] 30,
10, we propose a Target-guided Initialization Mechanism to construct the starting point for sampling by simultaneously considering the target domain features of the non-trust sample and isolating its semantic leakage that might disturb the semantic transformation. Secondly, we propose Semantic Feature Injection Mechanism that iteratively injects semantics related to the assigned label into the latent throughout the sampling trajectory via DDIM inversion [4 22 to ensure the semantic transformation without introducing unrelated domain features. Finally, for consistency of manipulated samples with the target distribution, we present a Domain-spert -sprcffii Feature Preservation Mechanism to actively inject target domain features with an adaptively perturbed latent drawn from the original non-trust sample.
Experimental results demonstrate that the proposed method achieves superior performance compared to state-of-the-ar (SOTA) methods across four standard SFDA benchmarks of different scales. Remarkably, our method successfully overcomes the limitations of existing methods in challenging domain adaptation scenarios involving large domain shifts. For instance, we achieve a gain of 9.3% %
2

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on D-+A and 8.2% on W-A tasks over the existing SOTA method on the small-scale Office- 31 dataset, and a remarkable gain of 18.6% over SOTA for the Rw-*CI task on the medium- scale Office-Home dataset. On the large-scale DomainNet-122 dataset, we achieve gain of 24,4 % over the existing generation-ben method and 6.3% over SOTA for the C-+P task.
The contributions of this paper are summarized as follows.
We propose DPTM, a novel framework for Source-free Domain Adaptation (SFDA) that progressively constructs and refines pseudo-target domain by leveraging unlabeled target data as references with a latent diffusion model.
We develop a manipulation strategy that leverages Target-guided Initialization, Semantic Feature Injection, and Domain-specifie Feature Preservation to semantically transform the unreliable sample toward the newly assigned label while preserving target-domain features.
We design Progressive Refinement Mechanism that progressively reduces the domain discrepancy between the pseudo target domain and the real target domain via iterative refinement.
Related Work
Source-Free Domain Adaptation. Source-free domain adaptation (SFDA) methods can be broadly
categorized into non non- gennereerttion and ceneration-based methods. Non-generation methods [20] 50 36
13
37 mainly employ self- training techniques using pseudo- labels predicted by the source model. However, the inherent unreliability of pseudo-labed caused by source-target domain shifts substantially limits their performance, especially in challenging DA scenarios with significant domain discrepancies. Generation based methods [23] [7 61
49
53 23
35
35
38 37
usually generate pseudo-source domains to convert SFDA into conventional UDA problem, but their performance remains constrained by the domain shift between the generated pseudo-source and target domains. Different from existing methods, we develop a novel method that directly generates pseudo-target domains and progressively reduces the domain shift between pseudo-target and real-target samples through iterative refinement to overcome the performance limitations.
Diffusion Models. Diffusion models [12 7 32 have become state-of-thh-art in many generative 14
3). Their exceptional generation capabilities and pre-trained visual knowledge have been successfully transferred to other vision tasks such as image segmentation [54] 59 and domain generalization LS 40). In this work, we leverage diffusion models to facilitate SFDA tasks by semantically transforming unreliable target samples toward their assigned category labels while rigorously preserving their target domain characteristics.
tasks [46 60
55
56
16
Diffusion models are latent variable generative models defined by a forward and reverse Markov process 77 The forward process {q: (EE,0 progressively adds Gaussian noise the data
Fo ~ go (o)) by (< Maxx; ayzo, I) where the scheduling syper-parameters of + o 1. The reverse process {Pr [ gradually removes noise using a learned denoiser cg. Starting from p(xT) (), it reconstructs xo through transitions pa mp 1/2z) -N(x,-1 It Ea (z+, f), oil). Conditional generation is achieved by incorporating condition y into the denoising process as an input to ea (Ix, y, l). Classifier-free guidance enables conditional generation by combining conditional and unconditional denoising predictions with a guidance scale 71
Ea (z,, t) = (1 + 71 cep (z,,9.
V. f) - 71 ca (I,, Ø,t).
3 Method
3.1 Overall Framework
Let Dere a labeled source domain with input space X'rre = {xp"<) Nere and label space Vere = M=i
(ure and Diry unlabeled target domain with input space Xrrg (xf" (x " where Nere and Narg denote the number of samples in the source and target domains, respectively. In SFDA, we first train a source model &ere Xare -> Vare on Dare via supervised learning, and then utilize Ustc and the unlabeled Kerg to learn a target model dorg Xeng Verg that generalizes well on Derg-
3

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Progressive Refinement
Non-trust Set
Target Data
Latent Diffusion
X]
Target Model
photo of jkd
X
Target-gnided Initialization
FFT
Uncertainty Division
IFFT
DDPM Forvarr
E
Ro
T;
Non-Trust Set
photo
Semantic Feature Injection
Z1
denoise 2t-1
DDIM Inversion
2
OH FFT
Ff
denuise
Zt
20 DDPM Forward
20.1
Trust Set
61
H
Domaln-speeffie Feature
Preservation
Figure 1: In DPTM, employ progressive refinement R. times: First, to make predictions on the target data. Based on each sample's 's prediction uncertainty, we divide the target data into trust and non-trust set. For the low-uncertainty trust set, train the target model using pseudo-labels in a supervised manner. For the high-uncertainty non-trust set, we assign label for each sample x)', employ manipulation strategy that semantically transforms x" toward class Dr. while preserving the targer-domain features of x²" Our manipulation consists of three components: Target-guided Initialization obtain effective sampling starting point, Semantic Feature Injection to convert the semantics of the generated sample to ÿt, and Domain-specific Feature Preservation maintain the generated sample within the target distribution.
the target model
Figure deppict the proposed framework that comprises three key components, including the partition of trust and non-trust for unlabeled target data in Section 3.2 manipulation strategy of the non-trust set in Section 3.3] and a progressive refinement mechanism that continuously minimizes the discrepancy between
evolving targetttrget domain and the real target domain in Section 3.4 We initialize the target model óirg with the pre-trained source model Φere- The target domain data is first partitioned into trust set V and a non-trust set 2/ based on prediction uncertainty. For any trust sample X V. we use the corresponding pseudo-label v as the supervision signal to train derg- The non-trust set undergoes diffusion- based manipulation produce l which combined with V to form the pseudo-targe domain D, V umm Finally, we optimize the source model &are this pseudo-target domain supervised manner, obtaining a target model f,,.
3.2 Trust and Non-frust Partition for Target Domain
Given any j-th unlabeled target data xj trg in Kerg. we first employ the target model dtrg to generate pseudo-label vf = arg max, Pot.,, (Melx(""), where Pe.., (xclx(5") = [p()] (1/x)) dirg)le denotes the probability corresponding
the c-th class in the output logits [p(y]x derg]] ]] of dirg. Existing research demonstrates that a small subset of pseudo-labels trustworthy, while the rest are intrinsi- cally unreliable (1 43 48 51 58). The uncertainty can be measured by entropy distinguish
reliable and unreliable pseudo-label labels 21 43 52 25] Therefore, compute the entropy H of the target model's prediction (y(|x""> @trg)) and divide Xiry into trust V and non-trust 24 using a threshold E. For sample x with H < E, we consider its pseudo label ye reliable and include (x trg y() in V. Otherwise, we solely include the sample in U. Ultimately we obtain the trust set V> [x] 108,0, v k of N, samples and non-trust set 24 {xx²) J,W, of Nu, samples.

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3.3 Manipulation of Non-trust Set
In this section, we develop a diffusion- based manipulation strategy to further exploit the target-domain information inherently encapsulated in the unreliable pseudo-labels for non-trust samples x" E U. For each x E Ld, we first uniformly assign category label ÿt mitigate potential class imbalance.
أ1 = (I mod [R4]/C1). Le (1. [ x C},
(2)
where C' is the total number of classes. Note that we discard the residual samples with | x I [24] for class balance. Subsequently, we achieve two objectives via a pre-trained diffusion model at the same time, i.e., i) semantic transformation of x toward the specified class iJ to convert ÿi into an effective supervisory signal, and ii) preservation of the target-domain features. The manipulated sample X maintains fidelity to the target distribution while exhibiting substantially improved class certainty to allow for converting problematic non-trust samples into useful training instances.
To this end, our diffusion-based manipulation strategy consists of three key components, i.e., i) Target- guided Initialization that extracts target domain guidance from x²" form effective starting point for the diffusion denoising process, ii) Semantic Feature Injection that ensures the designated class úJ for generated samples during denoising, and Domain-specific Feature Preservation that maintains the generated samples within the target distribution, as claborated below.
Target-guided Initialization. The sampling starting point Zy of diffusion models, particularly its low-frequency components, has been proven to significantly influence the generated image [45] 30 4717700 During inference, the oowffreqeency components of the generated image and starting point for sampling IT remain strongly correlated and diffusion models exploit signal leakage from these low-frequency components for image generation [10]. To generate novel sample from x that preserves target domain characteristics while conforming to the newly-assigned category in of x we propose incorporate the inherent domain-specifh features of x² into the starting point for sampling in diffusion models.
In domain adaptation and generalization, domain-specinn features are typically associated with the low-frequency components of x/'. Furthermore, to prevent the potential semantic leakage from the high-frequency component of x/", we extract the neutral random Gaussian noise la and the low-frequency component FLi from the input image x via Fast Fourier Transform (FFT).
component FlG from semantically
J4. FFT ( H, =JFT(()) @ HH)
where H a low-pass filter. Fzz. and FH are combined and inversely transformed via inverse FFT (IFFT) to produce a semantically neutral target-domain pseudo-image x
X = IFFT
(F F
x is first encoded into the latent space via an encoder E, and then subjected to a T-step DDPM forward process to add Gaussian noise. The noisy latent zr is used as the starting point for sampling.
2n = E(x)), 2T = Voržo + vr-ar,, c ~(,,
(5)
Semantic Feature Injection. For our task, the sampling starting point zr derived from (5) may inherently lack sufficient semantic relevance to due the following reasons. First, we construct the high-frequency components of 2r using a semantically neutral Gaussian noise image, which carries sr-related information. Secondly, although we isolate the high-frequency components of x", weak semantic leakage from x"" may persist, potentially conflicting with ÿt- Consequently, we may fail to semantically transform x²i ýi even with large guidance scale ?1 according to 22 To address this, we present semantic feature injection as below.
During denoising, each timestep f, adopt zigzag self-reflection operation following 22 We first denoise the latent 2, via the latent diffusion model to obtain 2, and then yield the refined latent ž by injecting ÿn relaeed semantic information into Z with DDIM inversion 34]
(V
2,
V
Oi
24 Voe
Eo (z Mn 1)
01-1
04
0t-1
(6)
Eo (zc-1, - 1) = (1 72) (z- Mr, 1) - 72E8 (2.-1, Ø,t - 1),
5

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where is the inversion guidance scale. According to (6), semantic alignment with ÿt is considered for the latents throughout the sampling trajectory. However, since DDIM inversion could introduce unrelated domain features, the latents could deviate from the target distribution when accumulating ii-- aligned semantic information. To address this, we selectively extract the high-frequency components carrying the accumulated semantic information and discard the low-frequency components harboring domain artifacts from ž, rather than directly leveraging ž;
F =FT 2.) @ (1- (1-H). H).
Fz is used as high-frequency semantics to aggregate with target domain specific features.
Pomain-specific Feature Preservation. To better align with the target distribution, we combine the high- freerrreeeney semantic features F the latents by DDIM inversion in 77) with the target domain specific features at each denoising timestep t. The domain-specinn
features are primarily encoded in the low-frequency components of samples from the target domain. To adapt to the time-varying noise level in Z₁, we perturb the clean latent 2, in (5) via the DDPM forward process with t-step Gaussian noise to generate 20, = 2a, + v1 QrE for timestep t. The low-frequency domain- specific features Fhu are extracted from Z0,t and combined with Fz to obtain enhanced latent ž that simultancously preserves Or-aligned high-frequency semantics and embeds target-domain low-frequency features to produce Z(-1.
= (Ft.. +
Fl.. FFT (20,z) @ H.
(8)
3.4 Progressive Refinement Mechanism
We design a progressive refinement mechanism iteratively refine the pseudo-targe' domain for R iterations to further optimize the target model. When optimized solely fixed pseudo-target domain, the target model could be affected by the trust set V inevitably contains residual label noise due to imperfect pseudo-labeling, and the manipulated non-trust set L gradually accumulates domain discrepancy in sample generation. Therefore, for any r-th (r 1, 1...... RR) refinement iteration, update the trust set V" to correct inaccurate pseudo-labels and reduce the size of the non-trust set lt for decreasing domain discrepancy. The target model Brg re- re-patitiions the target data into updated trust set D(++1) and non-trust set (((++)), (((+1) is then further manipulated according to Section 3.3 to generate 1m,.+++1) for constructing the refined pseudo-target domain Df (2(+))))m)m... (r+1)
The updated target model Pirg (r+1) is obtained by fine-tuning o! on D 11) We empirically find in Figure that, during progressive refinement, v(++1) provides more accurate pseudo-labels than vtr)
with [)++1)] |D(r) such that [z/m.(++1) < [c/m.(r) to reduce the size of manipulated non-trust
set and decrease the domain discrepancy in the pseudo-target domain. Compared with (r) Ping (r+1)
can better approximate the real target distribution and finally achieve enhanced performance.
4 Experiments
4.1 Experimental Settings
Datasets. We adopt four standard domain adaptation benchmarks of different scales for evaluations, including the small-scale Office-31 dataset (33) the medium-scale Office-Home dataset [41], and two large-scale datasets (i.e., VisDA 28 and DomainNet- (27]). Refer to the supplementary material for complete dataset statistics and domain configurations.
Comparative Methods. We compare with 21 existing methods from three distinct groups: i) the base- line results from the source model, ii) generation- based SFDA methods CPGA BT, ASOGE [6)
ISFDA 266. PS [9. DATUM [1 and DM-SFDA [4) and iii) non-generation SFDA methods includ-
ing current state-of-the-an SFDA methods SHOT 200, NRC (50.. GKD [3). HCL [13], AaD 44]
AdaCon (2). CoWA (18). SCLM 39]. ELR [53 PLUE 23
CRS 57) CPD (61) TPDS 35
DIFO B8). and ProDe 337]
Implementation Details. We employ stable-diffusion v1-5
32 as the diffusion model to generate
and 72 = (6). We set the threshold E 512x512 images with 20 denoising steps. 71 = 5.5 ①
to 0.01, and the total refinement iteration count R to 10. Note that setting E and R to other values

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Table Office-31 results (%) with ResNet--50. Methods with top three performance in each column are highlighted in red, orange, and yellow.
Method
Venue
A D
A-W D-
D-W
W--A
W-D
Avg.
Baseline method
Source
79.7
77.6
65.5
97.9
63.8
99.8
80.7
Geuerationn-asee method
CPGA E1
UCCA11
94.4
94.1
76.0
98.4
76.6
99.8
89.9
98.1
99.7
ISFDA 26
CVPR24
95.3
94.2
76.4
98.3
77.5
99.9
90.1
DM-SFDA
97.7
99.3
100.0
93.7
Nons-gcacration method
SHOT 20
ICML20
93.7
91.1
74.2
98.2
74.6
100.
88.6
NRC 50
NIPS21
96.0
90.8
75.3
99.0
75.0
89.4
GKD 86
IROS21
94.6
91.6
75.1
98.7
75.1
100.
89.2
HCL 1
NIPS21
94.7
92.5
75.9
98.2
77.7
100.
89.8
AdaCun 2
CVPR22
87.7
83.1
73.7
91.3
77.6
72.8
%1.0
90.3
ELR 53
ICLR23
93.8
93.3
76.2
98.0
76.9
100.
89.6
PLUE
CVPR23
89.2
88.4
72.8
97.1
69.6
97.9
85.8
CPD 61
PR24
96.6
94.2
77.3
98.2
78.3
100.
90.8
TPDS
35
IJC224
97.1
94.5
75.7
98.7
75.5
99.8
90.2
95.7
97.0
89.3
ProDe
17
ICLR25
94.4
92.1
79.8
95.6
79.0
98.6
89.9
DPTMo)
97.2
95.3
92.0
98.7
91.7
100.
95.8
may obtain superior performance. For the adaptation model, we employ ResNet-50 for Office-31 [33]
Office-Home [41] and DomainNet-122 (27), and ResNet-101 for VisDA (28). We train for 20K
iterations with the batch size of 128 and learning rate of 3e-3 for large-scale 1om2inNet-126 [27
and VisDA (28), and 15K iterations with the batch size of 32 and learning rate of le-3 for Office-31
and Office-Homee Home. Weight decay is set 5e-4 for all the datasets.
4.2 Main Results
Evaluations on Office-31. T that our method is superior to generation- based SFDA methods on Office-31 1 and outperforms the best generation- based SFDA method DM-SFDA 4 on average across all the DA tasks. Compared with non-generation methods, our method outperforms the best non-generation methods in all tasks except D D-W, delivering average accuracy gain of 5%. Notably, our method achieves significant improvements on challenging adaptation tasks: 9.3% on D- and 8.2% W-A. These results validate the effectiveness of our method.
Evaluations on Office-Homm and Visda. Table 2 shows that our method significantly outperforms existing SFDA methods on Office-Home and VisDA. On Office-Home, we achieve an average accuracy gain of 1 11.7% over the best generation-
SFDA method DM-SFDA 4 and 10. 1% over the current SOTA method ProDe [37] across all domain adaptation tasks. Remarkably, our method outperforms ProDe by 22.7%. 21.0%, and 21.6% on challenging Ar-CI,, Pr-+CI, and Rw Rw-CC tasks where existing methods usually perform poorly. On VisDA, our method achieves an average accuracy gain of 8.5% over ISFDA (26) and 8.2% ProDe (37 (see the supplementary material for details). These results strongly validate the effectiveness of our method in difficult domain adaptation scenarios.
Evaluations on DomainNet- 126. Our method achieves a 17.6% higher average accuracy than the generation- CPGA [I] and surpasses current SOTA ProDe [37 by 3.7% on DomainNet-1226 It significantly outperforms CPGA (31] across all domain adaptation tasks and exceeds ProDe 337 in most tasks, with only minor performance gaps in three DA scenarios.
4.3 Ablation Studies
We conduct ablation studies mainly on the Office-Home dataset. More ablation studies can found in the supplementary materials.
7

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Table Results (%) on Offe and VisDA. Office-Home evaluated with ResNet-50, and VisDA is evaluated with ResNet- 101. The top three performances in each column are highlighted in red, orange, and yellow, respectively.
Offece-Home
VisDA P RR
Method
Venue
Ar-
Baseline method
Source
50.1
67.9
74.4
55.2
65.2
67.2
53.4
44.5
74.1
64.2
51.5
78.7
62.2
63.5
Generation bused method
UCA121
78.1
75.5
ASOGE 6
TCSVT23
59.1
78.4
81.0
67.7
78.4
71.5
65.8
57.2
80.2
72.7
60.7
83.3
71.8
83.2
78.9
79.5
PS 9
ML24
57.8
77.3
81.2
68.4
76.9
78.1
67.8
57.3
82.1
75.2
59.1
83.4
72.1
84.1
DATUM
CVPR23
55.3
76.8
79.3
65.1
77.7
78.6
62.4
52.1
79.7
66.6
55.9
80.5
69.2
DM-SEDA
89.6
83.3
70.0
85.8
87.4
71.3
69.6
88.2
68.5
88.7
79.5
86.3
generation method
SHOT 20
ICML.20
56.7
77.9
80.6
68.0
78.0
79 4
67.9
54.5
82.3
74.2
58.6
84.5
71.9
82.7
NRC
NIPS21
80.3
82.0
79.8
78 6
83.0
58.6
85.6
85.9
GKD
X6
IROS21
56.5
78.2
81.8
68.7
78.9
79.1
67.6
$4.8
82.6
74.4
58.55
84.8
72.2
83.0
79.8
AdaCon
R
CVPR22
47.2
75.1
75.5
60.7
73.3
73.2
60.2
45.2
76.6
65.6
48.3
79.1
65.0
86.8
CoWA
18
ICML22
56.9
78.4
81.0
69.1
80.0
79.9
67.7
57.2
82.4
72.8
60.5
84.5
72.5
86.9
SCLM
39
NN22
58.2
80.3
81.5
69.3
79.0
80.7
56.8
82.7
74.7
60.6
85.0
73.0
85.3
ELR 53
ICLR23
58.4
78.7
81.5
69.2
79.5
79.3
66.3
58.0
82.6
73.4
59.8
85.1
72.6
85.8
CVPR23
78.2
73.5
78.6
88.3
CPD 61
PR24
59.1
79.0
K2.4
68.5
79.7
79.5
67.9
57.9
82.8
73.8
61.2
84.6
73.0
85.8
80.3
79.4
85.3
DIFO
38
CVPR24
62.6
87.5
87.1
79.5
87.9
87.4
78.3
63.4
88.1
80.0
63.3
87.7
79.4
88.6
ProDe
37
ICLR25
64.0
90.0
88.3
81.1
90.1
88.6
79.8
65.4
89.0
80.9
65.5
90.2
81.
88.7
DPTM(uurs)
86.7
94.2
92.8
91.5
94.0
92.6
90.6
86.4
92.8
90.5
87.1
94.7
91.2
97.6
Table Results (%) on DomainNet-126 evaluated with ResNet-50. The top three performances in each column are highlighted in red, orange, and yellow, respectively.
DomainNett122
Method
Venue
C-+P
C-
C-+S
P--C
P
P-SS
R-C
R-PP
R
S-+C
S-+P
S-R
Avg.
Baseline method
Source
75.3
47.8
57.5
61.1
48.6
63.5
56.2
59.5
56.4
Generation-base, method
CPGA
31]
IICA121
61.2
76.7
59.6
64.5
81.3
61.0
68.6
69.5
65.9
66.9
60.2
75.1
67.6
None-generation method
SHOT 20
JCML20
78.2
81.3
61.7
67.7
67.6
57.8
70.2
78.0
NRC 50
NIPS21
62.6
77.1
58.3
62.9
81.3
60.7
64.7
69.4
58.7
69.4
65.8
78.7
67.5
GKD 55
IROS21
61.4
77.4
60.3
69.6
81.4
63.2
68.3
68.4
59.5
71.5
65.2
77.6
68.7
AdaCon
CVPR22
60.8
74.8
55.9
78.3
58.2
63.1
68.1
55.6
67.1
66.0
75.4
65.4
CoWA 10
ICML22
64.6
80.6
60.6
66.2
79.8
60.8
69.0
67.2
60.0
69.0
65.8
79.9
68.6
CVPR23
65.9
53.8
TPDS
15
JCC224
62.9
77.1
59.8
65.6
79.0
61.5
66.4
67.0
58.2
68.6
64.3
75.3
67.1
DIPO
CVPR24
73.8
89.0
69.4
74.0
85.7
70.1
74.8
74.6
69.6
74.7
74.3
88.0
76.7
ProDe
37
JCLR25
79.3
91.0
75.3
80.0
90.9
75.6
80.4
78.9
75.4
80.4
79.2
91.0
81.5
DPTMiours)
85.4
Table 4: Ablation study results (%) on Different LDMs evaluated with R 3.
E E
0.001.
LDM
|Ar-CCAAr CI-AArCCW--CCC
+Arr --P Rw >Ar PP.
SDXL
69.4
82.2
82.8
82.6
82.2
65.8
67.0
82.4
71.1
67.5
75.6
SD15
67.0
83.6
83,9
70.6
84.3
82,9
65.6
63.6
84.6
69.6
66.4
85.0
75.6
Different Versions of Latent Diffusion Models. We conduct ablation studies using both Stable Dif-
fusion v1.5 (SD15) and Stable Diffusion XI. (SDXL.) [29), with identical parameters (F 0.001 and R = 3) except for output resolution SDXL natively generates x 1024 images while SD15 pro-

---

Table 5: Ablation study results (%) on Threshold E evaluated with R 10.
E
Ar-sCIAr Ar-r--Rw CI->ArCI- P Rw Pr >A Pr Rw -ow->AA AArw RC >Pr Avg.
0.001
74.7
85.9
87.5
75,0
88.5
85.6
72.1
73.3
87.2
75.2
74.6
88.8
80.7
0.005
81.7
92.6
90.8
85.5
92.1
88.2
85.5
81.2
88.3
82.1
79.9
92.1
86.7
0.01
86.7
94.2
92.8
91.5
94.0
92.6
90.6
86.4
92.8
90.5
87.1
94.7
91.2
L'dn's feeut basuiyn
(a)
(b)
(c)
(d)
(e)
Figure 2: Ablation Manipulation Mechanism of x/" Row: 0r 'Alar Clock', 'Curtains',
'Computer', 'Bottle', respectively. Column: (a) x (b) x w/o Target-guided Initialization (c) x w/o Semantic Feature Injection (d) x w/o Domain-specifc Feature Preservation (e) x of our method.
duces 512x512 images due to their architectural differences. Table shows comparable performance, but SDXL's higher computational cost makes SD15 our preferred choice for implementation.
Values of Threshold E. We also conduct ablation the Threshold E (0.001, 0.005, 0.01). 5 that appropriately increasing E may yield better results.
Manipulation Mechanism of Non-trust Set. As shown in Figure 2 our method's manipulated sam- ples x exhibit the best semantic alignment with their assigned labels ÿJ = and the best preservation of target distribution characteristics, detailed in the supplementary material.
4.4 Additional Analysis
We provide additional analyses to further validate the effectiveness of our method. More analysis can be found in the supplementary materials.
Analysis on Progressive Refinement Mechanism. For the performance of our method the Office-Home dataset shown in Table 2 we provide detailed performance trajectory as r increases from to 10, in order demonstrate the effectiveness of the proposed Progressive Refinement Mechanism. Firstly, we present experimental results for r {0, 2, 4, 6, 8, 10) in Table provide complete experimental results in the supplementary materials, where r 0 equivalent to using only the source model. Table Specifically, as increases, the performance of the target model first improves rapidly and then growth becomes slow. Secondly, we select the first 4 DA tasks Ar-ClI Art+Pr Ar- and Cl- and
6 shows that the performance of the target model improves
increases.

---

Table Full results of the performance trajectory r grows from to 10 on Office-Home evaluated with E = 0.01.
Ar +CI -Pr Ar RR CI -I-AA CI +Pr
>Rw Pr >Ar CII
RR Rw +Ar Rw
Rw
Pr Avg.
50.1
67.9
74.4
55.2
65.2
67.2
53.4
44.5
64.2
51.5
78.7
62.2
60.2
80.9
82.8
67.8
79.3
80.4
64.5
57.1
81.6
69.1
60.5
83.4
72.3
2
72.6
87.2
85.7
73.5
85.4
84.4
72.3
69.7
85.2
76.7
70.8
87.7
79.3
75.3
89.7
87.6
77.7
88.9
86.4
79.2
76.1
86.7
80.1
75.5
90.2
82.8
79.0
91.8
89.1
81.0
91.2
88.3
82.1
79.1
88.1
82.7
79.1
91.7
85.3
81.5
92.5
89.8
83.9
92.0
89.4
85.3
81.5
89.3
84.5
81.8
92.8
87.0
83.7
92.9
90.7
86.1
92.8
90.2
87.1
83.1
90.0
87.4
83.3
93.3
88.4
7
85.0
93.7
91.5
87.5
93.0
91.2
87.8
84.4
91.0
88.6
84.9
93.9
89.4
85.9
94.2
91.7
88.7
93.8
91.9
89.0
85.1
91.7
89.7
85.6
94.2
90.1
85.9
94.1
92.0
89.8
93.9
92.3
89.8
85.6
92.2
90.2
86.6
94.4
90.6
86.7
94.2
92.8
91.5
94.0
92.6
90.6
86.4
92.8
90.5
87.1
94.7
91.2
100
1000
3200
CLirk
Torbebee of
2000
1500
8
1000
8
(b)
Figure 3: The relationship between versus: (a) The number of samples in the trust (b) The trust
set accuracy. (c) The number of samples the non-trust set.
plot: the relationship between the number of samples in the trust set versus the trust accuracy versus т, and the number of samples in the non-trust set versus r. As shown Figure 3 two main conclusions be drawn: (1) The number of samples in the trust set increases significantly with while correspondingly, the non-trust set size decreases substantially. This indicates that the model progressively learns to make predictions with low uncertainty. (2) Overall, the trust set accuracy remains at a high level. Although relatively low across all four DA tasks at r 2, the accuracy shows significant recovery with increasing r, demonstrating our method's capability to progressively correct previous errors.
Analysis Trust and Non-trust Partition for Target Domain. We provide more analysis on the proposed Trust and Non-trust Partition for the target Domain, detailed in the supplementary materials.
Analysis on Manipulation of Non-trust Set. We provide more analysis on the proposed Manipula- tion of Non-trust Set, detailed in the supplementary materials.
5 Conclusion
We propose DPTM, novel generation-based framework that utilizes unlabeled target data as references to construct and progressively refine pseudo-target domain via the latent diffusion model for Source-free Domain Adaptation (SFDA). We first divide the target into a set based on prediction uncertainty. For the trust set, we directly train the target model with pseudo labels in a supervised manner. For the non-trust set, we assign label for each sample and propose manipulation strategy consisting of Target-guided Initialization, Semantic Feature Injection, and Domain- nomain-specifie Feature Preservation, which semantically transforms the high-uncertee uncertainty sample toward the assigned category, while maintaining the generated sample in the target distribution. We progressively refined this process which simultaneously corrects pseudo-label inaceuracies in the previous trust set and decreases domain discrepancy in the previous pseudo-target domain, iteratively improving the target model. Experimental results demonstrate that our method achieves state-of-the-. performance on SFDA classification.
set and non-trust
10

---

Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 62431017, Grant 62320 106003, Grant U24A20251, Grant 62125109, Grant 62120106007, Grant 6237 1237, Grant 62301299, Grant 62401366, 3666, Grant 62401357, Grant 62401367, and in part by the Program of Shanghai Science and Technology Innovation Project under Grant 24BC3200800.
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Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
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Reviews:
[{"review_id": "caXmfUMIbf", "reviewer": "Reviewer_Rmwo", "summary": "The paper proposes a method for Source-Free Domain Adaptation (SFDA) called Diffusion-Driven Progressive Target Manipulation (DPTM). The key idea is to leverage a pre-trained latent diffusion model (stable diffusion) to generate a pseudo-target domain from unlabeled target data. The target data are split into a “trust set” (high-confidence samples with reliable pseudo-labels) and a “non-trust set” (low-confidence samples) based on prediction entropy. The trust set samples are used directly with their pseudo-labels for supervised fine-tuning, while the non-trust samples are assigned new labels uniformly at random (to avoid class imbalance) and semantically manipulated into those categories using the diffusion model.\n\nThe manipulation through the diffusion model is carefully designed to preserve the target-domain characteristics of each image while changing its semantic class. DPTM employs a three-key strategy: \n1.\tTarget-guided initialization: initialize the diffusion sampling with a latent that retains the original image’s structural features,\n2.\tSemantic feature injection: iteratively guide the diffusion process toward the assigned label,\n3.\tDomain-specific feature preservation: inject features from the original image into the diffusion latent to maintain the target domain style.\n\nAfter this manipulation, the new generated images (with their assigned labels) together with the trust-set images form the “pseudo-target domain” used to train the target model. Importantly, DPTM is applied in progressive refinement iterations, i.e., after one round of adaptation, the target model is improved and can produce more confident pseudo-labels, so the trust set grows and the non-trust set shrinks. \n\nEmpirically, DPTM demonstrates state-of-the-art performances on 4 standard SFDA benchmarks of varying scales: Office-31, Office-Home, VisDA-2017, and DomainNet-126. It outperforms existing methods by substantial margins, especially on challenging transfers with large domain shifts.", "questions": "How exactly is the class condition provided to Stable Diffusion? The paper denotes conditioning on y, but since Stable Diffusion is typically text-conditioned, did the authors provide the class name as a text prompt? Or did they finetune an embedding for each class?\n\nHow is the training runtime of DPTM for each progressive refinement iteration or until producing good performances?\n\nI’m still not fully convinced that DPTM benefits from the random assignment of the labels for non-trust samples as a starting point, instead of the pseudo-labeling mechanism like on the trust samples. Starting with completely random labels for the non-trust set might hinder reproducibility and stability. Could the authors clarify on this?", "limitations": "Yes, the authors mention that the proposed method is inherently constrained by the generation capability of the diffusion models. \n\nHowever, some other potential practical burden of implementing such a multi-stage system is under-addressed. \n\nIt would be great to include a discussion on deployment challenges (e.g., memory/GPU needs, sensitivity to hyperparameters) and consider if the use of synthetic target features could inadvertently cause performance drift in critical systems.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**STRENGTHS**\n\n**Originality**: The paper offers a creative take on SFDA by utilizing diffusion models for style-aware augmentation. The two-stage framework and spatial calibration mechanism are technically sound and well-motivated.\n\n**Empirical performance**: In my opinion, this is the main strength of the paper. The proposed method produces strong results on 4 benchmark datasets with significant and consistent gains over prior work in the setting of SFDA. Extensive ablations demonstrate the contribution of each component.\n\n**Clarity**: The high-level intuition behind each design component is clear, especially in describing the use of diffusion for feature stylization and progressive learning from clean to noisy pseudo-labels.\n\n**WEAKNESSES**\n\n**Practicality**: The method introduces multiple components (e.g., multiple loss terms, a style sampler trained using DDIM, a learned confidence regressor, a spatial calibration module), which may be cumbersome to train in practice and require careful tuning.\n\n**Minor redactional issues**: Some parts of the paper suffer from broken English and formatting issues, e.g., \n- Abstract: “.. a challenging tasks …”\n- Page 6 (experiments): “Comperative Methods”\n- Appendix C: “Thess results demonstrate …”, “demnostrated”", "was_revised": true, "final_justification": "After considering all the responses in the rebuttal, my main concerns have been addressed, particularly\n- Training complexity is on-par with a few other existing, advanced SFDA methods -- the trade-off between accuracy and complexity is still reasonable.\n- Justification on applying random assignment of labels for non-trust samples through additional empirical observations.\n\nGiven that the performance gain induced by the proposed method is significant in the space of SFDA, I increase my final score to 5: Accept.", "rebuttal": "### Q1: Practicality, sensitivity to hyperparameters, and performance in critical systems\nWe appreciate the reviewer's insightful comment. In fact, we did not tune hyperparameters separately for each SFDA task. In lines 254-255, we intend to mean that we only explore different values of $E$ and $R$ once on Office-Home and fix $E$ to 0.01 and $R$ to 10 for all the experiments (except for ablation studies). The performance on benchmark datasets demonstrates that these hyperparameter values work for most cases. To validate the practicality of our methods in critical systems, we also tested the performance of our methods on real-world scenarios VLCS using unsupervised model selection without using target labels. We offer a detailed explanation in Response to Q1 by Reviewer mAks. \n\n### Q2: Minor redactional issues\nWe will correct these issues.\n\n### Q3: Class condition\nYes. We use the class name as the text prompt and use the pretrained Stable Diffusion without any finetuning.\n\n### Q4: Time cost and memory needs\nWe present a detailed analysis of the time cost and memory needs.\n1. Time cost:\nThe time cost can be mainly divided into two parts:\n* SFDA model training.\n  For any $r$-th refinement iteration with $r=1,2,...,R$, we train the SFDA model using the trust set and the manipulated non-trust set. The total number of samples of these two sets equals that of the target domain. We use supervised training here. The time cost mainly depends on the scale of the target domain, and remains constant as $r$ grows. We denote it as $t_{SFDA}$.\n\n* Sample generation.\n  For any $r$-th iteration refinement with $r=1,2,...,R$, the generation time for each sample remains constant, at about 1 second on a single NVIDIA Tesla V100. The generation time depends on the number of samples of the non-trust set, which is a subset of the target domain. Note that, with the growth of $r$, the size of the non-trust set declines rapidly, as shown in Figure 2. Thus, the total generation time for $R$ iterations equals the total number of generated samples $N_{total}$.\n  \n  The total training time $t$ can be estimated by $t= R*t_{SFDA}+N_{total}$. We take the 12 tasks in the Office-Home dataset shown in Table 1 as examples. We set $E=0.01$ and $R=10$ and run the tasks on a single NVIDIA Tesla V100. It takes about **7.4 hours for SFDA model training** and **3.8 hours for generation** to **complete the total algorithm** on average for each task, demonstrating an acceptable time cost. Note that our model also allows parallel training on multiple GPUs to further reduce the time cost.\n\n2. Memory needs:\nThe memory needs can be mainly divided into two parts:\n* SFDA Model Training.\n  During any $r$-th refinement iteration, the memory needs remain constant and approximately equal to those of standard supervised learning on the target domain, and will not accumulate as $r$ grows.\n* Sample Generation.\n  Our approach generates samples sequentially (one sample at a time). Thus, this part only requires sufficient memory to run the Stable Diffusion model.\nIn conclusion, our method does not require high total memory needs, and can be run comfortably on a single GPU with 24GB of memory.\n\n### Q5: Random assignment of labels for non-trust samples.\n\n**From a performance perspective**, we employ random label assignment, instead of using the original pseudo-labels of non-trust samples given by the SFDA model, for two key reasons. First, there are no obvious patterns between the original pseudo-labels and ground truth labels of non-trust samples, and the original pseudo-labels cannot provide valid semantic priors. Second, if we use the diffusion model to semantically transform non-trust samples to their original pseudo-labels, this approach could inevitably introduce the class imbalance issue in the manipulated non-trust set. Therefore, we adopt a uniform random label reassignment strategy as formalized in Equation (2). To validate this claim, we perform comparative experiments using the original pseudo-labels for generation. Due to time limits, the experiments were performed on the first 4 Office-Home tasks (Ar→Cl, Ar→Pr, Ar→Rw, and Cl→Ar). The results presented below demonstrate the validity of our choice.\n\n| Method        | Ar→Cl | Ar→Pr | Ar→Rw | Cl→Ar |\n|---------------|:---------:|:---------:|:---------:|:---------:|\n|Original pseudo-labels    |    80.3   |    91.1   |    90.1   |    83.9   |\n| Ours (randomly assigned labels)          |    86.7   |    94.2   |    92.8   |    91.5   |\n\nFrom the perspective of reproducibility and stability, the results are fully reproducible as long as the random seed is fixed. In this case, the division between trust and non-trust sets becomes repeatable, and the sample ordering in the non-trust set's data loader is reproducible. Consequently, the new labels assigned to non-trust samples via Equation (2) are perfectly reproducible."}, {"review_id": "PWVw9oedzN", "reviewer": "Reviewer_fShy", "summary": "The paper presents DPTM, a generation-based method for source-free domain adaptation that leverages diffusion models to transform images from non-trust sets of the target domain into new images assigned to different categories while preserving target domain characteristics. The approach incorporates three core techniques: Target-guided Initialization, Semantic Feature Injection, and Domain-specific Feature Preservation. The method demonstrates strong performance across multiple benchmarks, outperforming state-of-the-art SFDA approaches in image classification tasks.", "questions": "1. **Can you provide comprehensive ablation studies showing DPTM's performance when each component (Domain-specific Feature Preservation, Target-guided Initialization, Semantic Feature Injection) is removed individually?**\n2. **How does your targeted generation approach compare against methods that prioritize image diversity for target domain generalization, and can you provide experimental evidence supporting your design choice?**\n3. **How does DPTM compare against other diffusion-based domain adaptation methods like DATUM for instance or other similar approaches, and what specific advantages do your multiple techniques provide over simpler generation approaches?**\n4. **Can you provide more extensive qualitative results demonstrating the visual quality and domain adaptation effectiveness of your generated images?**", "limitations": "yes.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "**Strengths:**\n\n- **Writing Quality:** The paper is exceptionally well-written with clear, easily followable sections and well-motivated technical components.\n- **Comprehensive Evaluation:** The experimental comparison against existing methods is extensive and thorough, providing strong empirical validation.\n- **Technical Innovation:** The proposed techniques are ingenious in their dual approach of ensuring accurate generation of newly assigned classes while preserving essential domain-specific information within the images. The progressive refinement strategy represents a particularly compelling approach to the generation process.\n\n**Weaknesses:**\n\n- **Missing Component Analysis:** The method lacks quantitative ablation studies showing the individual impact of each technique. The paper should evaluate DPTM performance without Domain-specific Feature Preservation, Target-guided Initialization, and Semantic Feature Injection separately to understand each component's contribution.\n- **Diversity vs. Target Alignment Trade-off:** While generating target domain-aligned images is beneficial, the paper doesn't address whether increased image diversity might provide better generalization to the target domain. The authors need to discuss this trade-off and demonstrate experimentally that naive targeted data generation performs worse than their proposed approach.\n- **Incomplete Baseline Comparisons:** The paper omits comparison with existing generation methods that explicitly use diffusion models for pseudo-target domain population, such as DATUM [1]. Including these comparisons would strengthen the proposed technique and better justify the multiple components employed.\n- **Limited Qualitative Analysis:** The authors provide insufficient qualitative results to fully demonstrate the effectiveness and visual quality of their generation techniques.\n\n[1] Benigmim, Yasser, et al. \"One-shot unsupervised domain adaptation with personalized diffusion models.\" CVPR 2023.", "was_revised": true, "final_justification": "I appreciate the authors' thorough responses to my questions. They have addressed all concerns convincingly and went beyond the initial questions by offering additional insights into the effectiveness of their Progressive Refinement Mechanism. In light of these improvements, I am raising my score to accept.", "rebuttal": "### Q1: Comprehensive ablation studies\nWe have made ablation studies to evaluate each component on the Office-Home dataset, as reported below. The results demonstrate that all the components contribute significantly to the overall performance by obtaining average gains of 8.7%, 11.4%, and 9.0%, respectively.\n\n| Method                                   | Ar→Cl | Ar→Pr | Ar→Rw | Cl→Ar | Cl→Pr | Cl→Rw | Pr→Ar | Pr→Cl | Pr→Rw | Rw→Ar | Rw→Cl | Rw→Pr | Avg.  |\n|-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|-|\n| Ours                                     |    86.7   |    94.2   |    92.8   |    91.5   |    94.0   |    92.6   |    90.6   |    86.4   |    92.8   |    90.5   |    87.1   |    94.7   |  91.2 |\n| w/o Target-guided Initialization         |    75.9   |    89.4   |    88.3   |    78.9   |    87.9   |    87.0   |    76.0   |    74.1   |    87.8   |    80.0   |    75.9   |    89.1   |  82.5 |\n| w/o Semantic Feature Injection           |    72.9   |    88.0   |    84.1   |    77.5   |    88.3   |    83.6   |    76.8   |    69.0   |    84.0   |    75.7   |    68.8   |    88.4   |  79.8 |\n| w/o Domain-specific Feature Preservation |    70.2   |    89.9   |    85.5   |    81.1   |    89.4   |    89.3   |    80.7   |    70.7   |    86.3   |    80.3   |    72.1   |    91.0   | 82.2 |\n\n### Q2: Diversity vs. target alignment trade-off\nWe focus more on target alignment. To explore the diversity vs. target alignment trade-off, we directly use text-to-image generation (which focuses more on image diversity) to replace our generation mechanism of non-trust set, and perform the same iteration refinement. Due to time limits for rebuttal, we perform experiments on the first 4 Office-Home tasks (Ar→Cl, Ar→Pr, Ar→Rw, and Cl→Ar). As shown below, our method prioritizing target alignment is superior to the method prioritizing image diversity.\n\n| Method        | Ar→Cl | Ar→Pr | Ar→Rw | Cl→Ar |\n|---------------|:---------:|:---------:|:---------:|:---------:|\n| Text-to-image |    66.6   |    87.5   |    86.4   |    82.1   |\n| Ours          |    86.7   |    94.2   |    92.8   |    91.5   |\n\n### Q3: Incomplete baseline comparisons\nIt is not feasible to directly compare our method with DATUM for two reasons. First, DATUM addresses UDA where source data is accessible, while we focus on SFDA without access to source data. Second, DATUM is for segmentation, while we specialize in classification.\n\nTo address the concern, we include DM-SFDA [1] for comparison. As far as we know, DM-SFDA is the only existing generation-based SFDA method. It focuses on pseudo-source generation employing multiple stages of finetuning and generation. Since the source code of DM-SFDA is not available, we compare it with our methods on Office-31 (ResNet-50), Office-Home (ResNet-50), and VisDa (ResNet-101) by directly citing their results reported in [1]. The results demonstrate that our method significantly outperforms DM-SFDA on the majority of all SFDA tasks.\n\n\n\n\n\n| Method  | Office-31 |      |      |      |      |       |      |\n|---------|:---------:|:----:|:----:|:----:|:----:|:-----:|:----:|\n|         |    A→D    |  A→W |  D→A |  D→W |  W→A |  W→D  | Avg. |\n| DM-SFDA |    97.7   | 99.0 | 82.7 | 99.3 | 83.5 | 100.0 | 93.7 |\n| Ours    |    97.2   | 95.3 | 92.0 | 98.7 | 91.7 | 100.0 | 95.8 |\n\n| Method  | Office-Home |       |      |      |       |       |       |        |       |        |       |       |          |\n|---------|:-----:|:-----:|:----:|:----:|:-----:|:-----:|:-----:|:------:|:-----:|:------:|:-----:|:-----:|:--------:|\n|         |Ar→Cl | Ar→Pr | Ar→Rw | Cl→Ar | Cl→Pr | Cl→Rw | Pr→Ar | Pr→Cl | Pr→Rw | Rw→Ar | Rw→Cl | Rw→Pr | Avg.  |\n| DM-SFDA                                     |    68.5  |    89.6   |    83.3   |    70.0   |    85.8   |   87.4   |   71.3   |    69.6   |    88.2   |    77.8   |    68.5   |    88.7   |  79.5 |\n| Ours                                     |    86.7   |    94.2   |    92.8   |    91.5   |    94.0   |    92.6   |    90.6   |    86.4   |    92.8   |    90.5   |    87.1   |    94.7   |  91.2 |\n\n\n| Method  | VisDA |       |      |      |       |       |       |        |       |        |       |       |          |\n|---------|:-----:|:-----:|:----:|:----:|:-----:|:-----:|:-----:|:------:|:-----:|:------:|:-----:|:-----:|:--------:|\n|         | plane | bcycl |  bus |  car | horse | knife | mcycl | person | plant | sktbrd | train | truck | Perclass |\n| DM-SFDA |  98.1 |  89.8 | 90.6 | 90.5 |  96.8 |  95.2 |  92.2 |  93.4  |  97.8 |  94.4  |  92.4 |  48.8 |   86.3   |\n| Ours    |  99.5 |  97.1 | 96.2 | 93.0 |  99.2 |  99.2 |  98.8 |  97.7  |  99.3 |  99.7  |  98.6 |  93.1 |   97.6   |\n\n\n[1] Chopra S et al. Source-free domain adaptation with diffusion-guided source data generation. arXiv:2402.04929.\n\n### Q4: Limited qualitative analysis.\nWe conduct extra qualitative analysis in three parts: more generated samples, Grad-CAM of the SFDA model, and t-SNE for the SFDA model features. But according to the requirements of rebuttal, we will add them to the later version."}, {"review_id": "iTk6AkWhza", "reviewer": "Reviewer_pUPg", "summary": "-\tIn the source free domain adaptation (SFDA) task that uses only unlabeled target data and a source model pretrained with labeled source data, the authors propose a generation-based framework named Diffusion-Driven Progressive Target Manipulation (DPTM) to effectively utilize the non-trust set with high uncertainty among the target model's prediction results in the target domain by supplementing it with a diffusion model.", "questions": "-\tWhen generating samples of the target domain and applying them to train the target SFDA model, it seems that the size of the target SFDA model will have a great impact on the performance. Is there any analysis related to this?\n-\tA description and classification of the types of benchmark methods is needed.\n-\tThe performance improvement is quite extraordinary, and theoretical analysis is essential. The proposed method should quantitatively measure how well the samples generated by the diffusion model represent the target domain.\n-\tHow specifically was the grid search performed to find threshold 𝐸?\n-\tAt line 255, it says ‘we do not specifically optimize the search space’. What does this mean?", "limitations": "-\tThe limitations of the proposed technique are stated in the Appendix.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n-\tThe proposed method of combining the diffusion model with SFDA significantly improves the performance of SFDA on existing datasets.\n-\tIt is an ingenious approach to use the existing widely used DA technique based on the idea that low-frequency of an image represents domain information and high-frequency represents semantic information, to the starting point data of the diffusion model.\n\nWeaknesses\n\n-\tThe proposed method aims at SFDA, but it seems unfair to compare the performance with existing SFDA methods because it additionally uses a diffusion model with high computational cost in addition to the source model.\n-\tThe computational complexity of the proposed method is expected to be considerably high, and a detailed analysis is required, including comparison with benchmark methods.\n-\tThe reliability of the proposed method's entropy-based Trust and Non-trust Partition is not high. Therefore, the evaluation of the reliability of the entropy-based Trust and Non-trust Partition is required. In fact, the pseudo labeling accuracy of the two sets of needs to be evaluated, and the SFDA performance of the proposed method needs to be evaluated depending on the partition accuracy.\n-\tThe performance improvement is unrealistically high, so it is necessary to present and compare the performance when the model is trained with all samples from the target domain. In addition, considering the current performance improvement of the proposed method, it is expected that the performance of the proposed method should be higher when all samples from the target domain are set as non-trust set.\n-\tThe supplementary material contains repeated experimental results from the manuscript and does not contain the necessary new experimental results mentioned above. In particular, there should be a wider range of examples of samples generated via the Diffusion model in the target domain", "was_revised": true, "final_justification": "Through the rebuttal process, we have confirmed all of the following. \n1. The computational complexity of the proposed method\n2. Performance variation depending on target model size\n3. The reliability of the proposed method's entropy-based Trust and Non-trust Partition\n\n\nBased on this, we have decided to maintain our original decision (4: Borderline accept).", "rebuttal": "### Q1: Unfair comparison\n\n* A prevalent paradigm involves employing generative models to synthesize data, as target domain data scarcity is a fundamental challenge in SFDA settings. Consistent with other generation-based SFDA methods, our method strictly adheres to SFDA settings by completely avoiding any use of source domain data during the generation process.\n* The use of external models for adaptation is common in SFDA, as demonstrated by established methods like DIFO and ProDe in Section 4.1.\n* We only utilize the diffusion model for adaptation training. In the inference stage, we do not use the diffusion model.\n\n### Q2: Cost analysis\n\nPlease refer to the Response to Q3 by Reviewer Rmwo for detailed cost analysis.\n\n### Q3: Reliability of entropy-based trust and non-trust partition\nEntropy-based selection of reliable pseudo-labels of target samples is commonly used in SFDA, and its effectiveness is demonstrated in prior studies [1]. To further evaluate its reliability, we report the trust set accuracy evolving with $r$ on the Office-Home dataset as below ($r$ denotes the $r$-th refinement iteration, where $r=1,2,...R$, and in our experiments we set $R=10$). Figure 2(a) shows that the size of the trust set grows as $r$ increases. Remarkably, the trust accuracy remains consistently high across all tasks using a total of 10 refinement iterations. Besides, when $r$ is small, the trust set accuracy may not be high, but will increase to a high value as $r$ grows. For example, in tasks like Pr→Ar and Pr→Cl, trust accuracy is below 90% when $r=2$ but reaches higher than 97% when $r=10$. These results validate the effectiveness of our method that could correct errors in the trust set with the growth of $r$. Note that the non-trust set accuracy does not affect the performance of our method, as we completely discard the original pseudo-labels of non-trust samples.\n\n\n| $r$ | Ar→Cl | Ar→Pr | Ar→Rw | Cl→Ar | Cl→Pr | Cl→Rw | Pr→Ar | Pr→Cl | Pr→Rw | Rw→Ar | Rw→Cl | Rw→Pr |\n|-----|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|\n| $1$  | 95.4  | 97.6  | 98.5  | 99.1  | 100.0 | 99.8  | 100.0 | 100.0 | 99.8  | 100.0 | 97.4  | 99.1  |\n| $2$  | 85.3  | 95.0  | 97.2  | 88.8  | 93.9  | 96.1  | 85.9  | 83.8  | 96.6  | 87.5  | 86.4  | 96.3  |\n| $3$  | 94.5  | 98.3  | 97.6  | 91.2  | 98.2  | 97.3  | 90.0  | 91.9  | 97.8  | 92.9  | 93.1  | 98.2  |\n| $4$  | 94.2  | 98.0  | 98.1  | 94.3  | 98.1  | 97.8  | 93.6  | 95.2  | 98.0  | 94.4  | 95.7  | 98.4  |\n| $5$  | 96.6  | 99.0  | 98.7  | 95.1  | 99.0  | 98.5  | 94.4  | 95.8  | 98.2  | 95.6  | 95.7  |  99   |\n| $6$  | 96.3  | 99.1  | 98.6  | 95.7  | 99.1  | 98.6  | 96.1  | 96.8  | 98.6  | 95.0  | 97.3  |  99   |\n| $7$  | 97.1  | 99.4  | 98.5  | 96.9  | 99.4  | 98.5  | 97.1  | 96.8  | 98.7  | 96.8  | 96.6  | 99.2  |\n| $8$  | 97.2  | 99.3  | 99.2  | 97.0  | 99.2  | 99.2  | 96.6  | 97.9  | 98.8  | 95.7  | 97.2  | 99.5  |\n| $9$  | 98.6  | 99.5  | 99.2  | 97.0  | 99.6  | 99.2  | 97.0  | 98.1  | 98.9  | 97.3  | 97.8  | 99.3  |\n| $10$ | 97.5  | 99.4  | 98.7  | 97.5  | 99.6  | 99.1  | 97.2  | 97.7  | 99.4  | 97.3  | 98.5  | 99.5  |\n\n[1]Liang J et al. Source data-absent unsupervised domain adaptation through hypothesis transfer and labeling transfer. TPAMI 2021.\n\n### Q4: Set all target samples to non-trust set.\nThe high performance gain of our method stems from two key mechanisms: i) progressively expanding the trust set with high-accuracy pseudo-labels to allow the SFDA model to learn real target domain features, and ii) progressively reducing the manipulated non-trust set. Mechanism ii) is critical to prevent features from the synthetic domain becoming dominant, since there is an inherent gap that persists between the synthetic and real target domains (even though we employ alignments to bridge the gap). This phenomenon fundamentally stems from the inherent domain gap between synthetic and real data, a well-documented challenge that has been rigorously demonstrated in prior work [2].\n\nTherefore, canceling the trust set could cause degraded performance, since we could only obtain features from the synthetic domain. We validate it with an empirical study on the first 4 Office-Home tasks (Ar→Cl, Ar→Pr, Ar→Rw, and Cl→Ar), where we cancel the trust set and set all target samples to the non-trust set.  $r$ denotes the $r$-th refinement iteration, and we report results of $r=1,2,...,5$. As shown below, when using only non-trust samples, the performance is not improved as $r$ grows. \n\n|     | Ar$\\to$Cl | Ar$\\to$Pr | Ar$\\to$Rw | Cl$\\to$Ar |\n|-----|:---------:|:---------:|:---------:|:---------:|\n| source |    50.1   |    67.9   |    74.4   |    55.2   |\n| $r=1$ |    56.4   |    78.7   |    81.7   |    63.0   |\n| $r=2$ |    55.9   |    78.5   |    81.4   |    62.8   |\n| $r=3$ |    55.9   |    78.3   |    81.6   |    62.7   |\n| $r=4$ |    55.8   |    78.1   |    81.4   |    62.7   |\n| $r=5$ |    55.7   |    77.9   |    81.3   |    62.7   |\n\n[2] Akagic A et al. Exploring the Impact of Real and Synthetic Data in Image Classification: A Comprehensive Investigation Using CIFAKE Dataset. CoDIT 2024.\n\n### Q5: Appendix.\nWe will remove repeating experimental results and add more generated samples.\n\n### Q6: The size of the target SFDA model.\nTable A.1 shows results on VisDA with ResNet-101. Below, we provide extra results with ResNet-50 to further demonstrate the superior performance of our method.\n\n| Method     | plane | bcycl |  bus |  car | horse | knife | mcycl | person | plant | sktbrd | train | truck | Perclass |\n|------------|:-----:|:-----:|:----:|:----:|:-----:|:-----:|:-----:|:------:|:-----:|:------:|:-----:|:-----:|:--------:|\n| Source-R50 |  79.7 |  35.5 | 44.5 | 63.8 |  62.0 |  25.0 |  86.9 |  26.6  |  77.6 |  30.0  |  94.7 |  12.7 |   53.3   |\n| Ours-R50   |  99.5 |  96.1 | 93.5 | 82.5 |  98.3 |  99.2 |  96.3 |  96.8  |  98.8 |  98.5  |  98.1 |   81  |   94.9   |\n\n### Q7: Description and classification of the types of benchmark methods.\nWe will further classify and reorganize benchmark methods.\n\n### Q8:  Quantitatively measure how well the samples generated by the diffusion model represent the target domain.\n\nWe conduct cross-domain validation by training models on real target domain data and evaluating them on i) our generated target domain (1st row) and ii) Other Office-Home domains for reference (2nd row). For example, the model trained on real Art domain data (first column) achieves 84.2% accuracy on our synthetic Art samples, and 64.1% average accuracy on Clipart, Product, and RealWorld domains. The results indicate that our generated samples closely approximate the real target domain, and the domain gap between synthetic and real data is significantly smaller than the natural gap between Office-Home domains.\n\n|     | Ar | Cl | Pr | Rw |\n|-----|:---------:|:---------:|:---------:|:---------:|\n| real target->synthetic target             |   84.2\t|81.4\t    |87.9\t    |89.4   |\n| real target->other Office-Home domain     |   64.1    |\t62.5|\t57.3|\t64.8  |\n\n\n### Q9: How specifically was the grid search performed to find threshold $E$?\nWe use fixed values that $E=0.01$ and $R=10$ for all the results in Table 1-3 without any change. We only perform grid search once on Office-Home to explore $E\\in[0.01, 0.007, 0.005, 0.001]$ and $R\\in[1,...,10]$ and then fix the values $E=0.01$ and $R=10$ for all the datasets. We offer a detailed explanation in the Response to Q1 by Reviewer mAks, and will clarify it in the final version.\n\n### Q10: What does ‘we do not specifically optimize the search space’ mean?\nWe intend to mean we do not exhaustively search all the datasets for optimal values of $E$ and $R$, as specified in response to Q9. In fact, using larger $E$ or $R$ (*e.g.*, $E>0.01$ or $R>10$) could obtain better results."}, {"review_id": "I5EX8X7H4x", "reviewer": "Reviewer_LADn", "summary": "This paper presents a method called Diffusion-Driven Progressive Target Manipulation (**DPTM**) to tackle the challenge of source-free domain adaptation (SFDA). Specifically, DPTM partitions the target data into trusted and non-trusted sets based on the uncertainty of pseudo-label predictions, quantified by the entropy of the model outputs. A progressive training strategy is then introduced to iteratively refine the model and enhance adaptation performance.", "questions": "1. While it is common to use Fast Fourier Transform (FFT) to extract domain-specific features, I am also curious about the effectiveness of using an image encoder for this purpose. It would be helpful if the authors could provide some insight into how the results compare between these two approaches.\n\n2. I am wondering a more detailed comparison between this work and generation-based SFDA methods. Specifically, generating features and generating samples are fundamentally different, particularly in terms of computational cost. As far as I am aware, the generation-based methods listed in this paper do not generate samples. \n\n3. It is unclear why similar colors are used for target data and target-guided initialization, as well as for target model and semantic feature injection in Figure 1. This similarity may cause confusion and reduce the overall clarity of the figure. Additionally, it could benefit from a clearer and more informative layout. For example, explicitly highlighting the interaction relationships among different color blocks would enhance the reader's understanding.", "limitations": "The major limitation is similar to the first weakness mentioned above.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 1, "originality": 2, "strengths_and_weaknesses": "Strenths:\n\n1. **Good Performance**. The proposed DPTM achieves state-of-the-art performance across several widely used SFDA benchmarks, including Office-31, Office-Home, VisDA, and DomainNet-126. In particular, DPTM achieves an avearge classification accuracy of 91.2% on Office-Home, largely surpassing previous approches.\n\n2. **Clear Presentation**. The presentation of the ideas, framework, and experimental results is clear, making the paper easy to read and follow.\n\nWeaknesses:\n\n1. **Limited Contribution to the Community**. \nOne of my main concerns is that in the current era of large multimodal language models (MLLMs), the challenge of domain adaptation has been significantly mitigated by their powerful generalization capabilities. While I acknowledge that domain adaptation still holds practical value in specific application areas with insufficient data, such as the medical domain. Furthermore, the proposed method appears to be a straightforward combination of existing techniques, such as leveraging uncertainty from model outputs, employing a strong image generation model, applying FFT, and iteratvie refinement in , to improve performance. While I appreciate the impressive performance demonstrated on the Office-Home dataset, I believe the paper lacks sufficient novelty and theoretical contribution to meet the standards for acceptance at a top-tier conference like NeurIPS.\n\n2. **Similarity to Existing Work**. \nUsing a powerful diffusion model to generate pseudo-labeled samples and incorporating them as external data for training has already been explored, such as [1] and [2].\n\n3. **Lack of Visualization Results**.\nIt is recommended to include some visualization results illustrating the domain discrepancy. These visualizations would help support the claim that the proposed method effectively reduces domain discrepancy, as performance improvement alone provides only a limited perspective.\n\n[1] Chopra, Shivang, et al. \"Source-free domain adaptation with diffusion-guided source data generation.\" arXiv preprint arXiv:2402.04929 (2024).\n\n[2] Peng, Duo, et al. \"Unsupervised domain adaptation via domain-adaptive diffusion.\" IEEE Transactions on Image Processing (2024).", "was_revised": true, "final_justification": "The authors have provided clarifications and experiments that address most of my concerns, so I raised my score to 4.", "rebuttal": "### Q1: Limited contribution to the community.\nFirst of all, we have to emphasize that we provide in this paper a novel paradigm that leverages diffusion-based generation to directly approximate the target domain itself. This paradigm significantly differs from existing methods that rely heavily on pseudo-labeling or attempt to generate pseudo-source data and overcomes the intrinsic domain gap by nature. Experimental results also demonstrate incredibly superior performance as strong support of our paradigm. \n\nIn the field of domain adaptation, SFDA usually suffers from degraded performance caused by the lack of source data, which has not yet been addressed by existing methods. In this paper, we provide the key insight of leveraging diffusion-based generation to directly approximate the target domain rather than being restricted by making up the missing source data. We then realize this insight in a robust and generalizable manner and achieve strong empirical performance across diverse datasets. More importantly, this insight itself may open up new research directions for DA beyond SFDA, including broader scenarios such as UDA or even settings where the source model is entirely inaccessible. We have also made a detailed explanation on the practicality of our method in Response to Q1 by Reviewer mAks.\n\n### Q2: Similarity to Existing Work.\nIn SFDA, generating extra samples is a popular research paradigm due to the inherent data scarcity. Nevertheless, the fact that our method follows this paradigm should not compromise its novelty. The two works cited by the reviewer are **fundamentally different from our method**.\n\n* DM-SFDA: This method follows a pseudo-source data generation paradigm. We have already discussed the key differences between our approach and pseudo-source-based SFDA methods in Section 1. In terms of implementation, DM-SFDA relies heavily on complex fine-tuning of a diffusion model, which is totally different from our mechanism. Moreover, its performance is significantly lower than that of our method, as reported in Response to Q3 by Reviewer fShy.\n\n* DAD: **This work addresses UDA with source data, rather than SFDA without source data.** In fact, it focuses on leveraging source domain data effectively, while our method operates under a source-free setting where the source data is completely inaccessible, and we do not attempt to generate pseudo-source data.\n\nTo the best of our knowledge, there are no existing SFDA methods that adopt the same design or similar specific mechanisms as ours.\n\n\n### Q3: Lack of Visualization Results. \nThanks for the suggestion, we first conduct Grad-CAM visualization using our SFDA model, and compare the results with those obtained by the source model. The results demonstrate that the attention of our model focuses on the target object, instead of other unrelated regions. Besides, we conduct t-SNE analysis on target domain feature distribution, and our method exhibits the least category aliasing.\n\n\n### Q4: Using an image encoder to replace FFT.\nWe use FFT to obtain a pseudo-image in the Target-guided Initialization stage and a desirable latent in both Semantic Feature Injection and Domain-specific Feature Preservation. In this case, using an image encoder introduces several challenges:\n* First, the extracted features often have altered dimensions, making it difficult to reconstruct the original pseudo-image or latent representation without an accompanying decoder. \n* Second, this necessitates pretraining both the image encoder and decoder to extract and reconstruct frequency components. However, it is hard to guarantee effective pretraining using the unlabeled target data, especially on small-scale datasets. This can significantly degrade the performance of our method, while adding extra complexity and computational costs.\n\nIn contrast, our experiments demonstrate that FFT is simple yet effective. Even in real-world scenarios, it introduces negligible overhead and does not hinder the overall performance of the algorithm. We have evaluated the performance of our methods on real-world scenarios VLCS, using unsupervised model selection without using target labels, as elaborated in Response to Q1 by Reviewer mAks. \n\n### Q5: Generation-based methods listed in this paper do not generate samples.\nWe list 4 generation-based methods, including CPGA, ASOGE, ISFDA, and  PS. Actually, two of them, *i.e.*, ISFDA and PS, generate samples.\n\n### Q6: Similar colors are used in Figure 1.\nWe will correct the confusion in Figure 1."}, {"review_id": "uUcfyoHDXJ", "reviewer": "Reviewer_mAks", "summary": "This paper proposes a novel approach to the unsupervised source-free domain adaptation problem by leveraging diffusion models to transform unlabeled target images into images of specified categories. Specifically, the method first separates given target samples into a trust set and a non-trust set based on predictive uncertainty. For the samples in the non-trust set, a diffusion model is used to transform them into labeled images while preserving the characteristics of the target domain. By iteratively repeating this process, the authors demonstrate that their method achieves strong domain adaptation performance even in scenarios with a large source-target domain gap.", "questions": "* The diffusion process relies on the assumption that for a sufficiently large $T$, the latent variable $z_T$ follows a Gaussian distribution. However, Eq. (5) assumes that low-frequency information remains in $z_T$. I am curious whether there are specific conditions under which this assumption holds.\n* In the progressive refinement mechanism described in Section 3.4, the manipulation is repeatedly applied to all samples in $U^{r+1}$. However, this manipulation process appears to be independent of the target model $\\phi^r_{trg}$, then already manipulated samples can be reused. Would it not be more reasonable to apply the manipulation only to the newly added samples in $U^{r+1}$? Does this design choice have any impact on the overall performance?\n* The proposed method is based on the assumption that domain-specific and semantic information can be separated via FFT. While this may reasonably hold for the benchmark datasets used in the paper, it is unclear whether the same assumption would remain valid for real-image benchmarks that are commonly used in SFDA, such as TerraIncognita or VLCS.", "limitations": "* The authors appropriately acknowledge the limitation that their method relies on a pretrained diffusion model. However, as noted in the weaknesses section, it is also important to discuss the limitations concerning the practical applicability of the proposed approach. In particular, I am concerned that without a clearly defined model selection procedure under the unsupervised SFDA setting, the method may yield results that do not generalize beyond the specific benchmarks used.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "* Strengths\n  * This work explores a novel approach that leverages the powerful generative capability of pretrained diffusion models to convert given unlabeled target samples into labeled target samples. To preserve target-domain-specific features during this transformation, the authors propose several FFT-based techniques.\n  * The paper evaluates the proposed method across a range of benchmark datasets and compares it with various existing unsupervised SFDA approaches, focusing on scenarios with significant domain shifts.\n\n* Weaknesses\n  * The most significant concern with this work lies in its practical applicability to real-world scenarios. The proposed method involves several hyperparameters, such as the uncertainty threshold $E$, FFT-related parameters, and guidance scales $\\gamma_1$ and $\\gamma_2$, but the paper does not discuss how these values are determined. By definition, in unsupervised SFDA, these hyperparameters must be chosen using only the unlabeled target samples. However, the paper omits any discussion of this critical issue. While prior works have often reported results using hyperparameters tuned based on target-domain performance, I believe this convention should not be followed, as it entirely neglects the practical usability and generalizability of the method. There is a growing body of literature addressing hyperparameter selection in the context of SFDA, and at the very least, the authors should evaluate whether their method remains effective when such methods are employed. Relevant references include:\n    * Saito et al., “Tune it the right way: Unsupervised validation of domain adaptation via soft neighborhood density,” ICCV 2021\n    * Hu et al., “Mixed samples as probes for unsupervised model selection in domain adaptation,” NeurIPS 2023\n    * Lee et al., “Few-shot fine-tuning is all you need for source-free domain adaptation,” arXiv:2304.00792\n  * I also have concerns regarding the reported effectiveness of the proposed method. Related to the above point, the ablation study on the uncertainty threshold $E$ clearly shows how sensitive the performance is to hyperparameter choices. For instance, Table 6 shows that when $E=0.001$, the average performance drops to 80.7, at which point the method no longer shows meaningful improvements over existing approaches. Additionally, in the performance comparisons, results from baselines such as ProDe appear selectively chosen. ProDe, for example, reports significantly higher performance when using a ViT-CLIP backbone, which is not shown in this paper. This undermines the objectivity of the evaluation and should be avoided.\n  * The proposed method involves several complex components that serve similar functions, yet the effectiveness of this design is only qualitatively illustrated (e.g., in Figure 3). A more thorough, quantitative component-wise analysis is necessary to provide a clearer understanding of the contribution of each part of the method.", "was_revised": true, "final_justification": "The experimental and analytical results, along with the detailed explanations provided by the authors during the rebuttal process, have addressed many of my concerns about the paper. I hope that the new results presented by the authors will be properly incorporated into the revised version. In particular, I believe that the results on unsupervised model selection are valuable to support the practical applicability of the method.", "rebuttal": "### Q1: Hyperparameter selection procedure and real-world scenarios performance.\nWe thank the reviewer’s insightful comments regarding the practical applicability of our method in real-world scenarios, particularly in the hyperparameter selection procedure. We respond to this concern from two aspects:\n#### 1. Hyperparameter determination.\nWe would like to clarify the determination of hyperparameters with a more detailed description. In fact, we did not tune hyperparameters separately for each SFDA task. The guidance scales and FFT-related hyperparameters are fixed on all the datasets, and we also use $E=0.01$ and $R=10$ for all datasets. In lines 254-255, we intend to mean that we only explore different values of $E$ and $R$ once on Office-Home and fix $E$ to 0.01 and $R$ to 10 for all the experiments (except for ablation studies). Thus, the hyperparameter selection is practical for applications in real-world scenarios. We will further discuss the practicality of this hyperparameter below and validate it on VLCS.\n\n#### 2. Recommendations for real-world usage.\nWe fully agree that hyperparameter determination can be hard in real-world scenarios. Thus, we provide practical recommendations for hyperparameter selection. \n* For **default settings**, we suggest using the same hyperparameters as we do. \n* For **adjustment strategy**, if the model does not perform well, we first recommend moderately increasing both $E$ and $R$ (*e.g.*, $E=0.1$ and $R=20$). Increasing $E$ and $R$ is probably effective in most cases, according to Section 4.3.\n* For more complex scenarios, we can also explore other hyperparameter values. In such cases, **unsupervised model selection** becomes necessary. Inspired by the reviewer, we employ an unsupervised model selection strategy using nuclear norm. We extract the softmax output matrix for all target samples and calculate its nuclear norms. Then， we select the model with the largest nuclear norm [1][2]. As suggested, we train models with $E\\in[0.01,0.005,0.001]$ and $R$ from 1 to 10 on real-world benchmark **VLCS** and apply this model selection metric. In the table below,  comparison results are from [3], where UMS is **unsupervised model selection** for short. Note that all the methods for comparison report their best target accuracy (according to the labels), **while ours performs model selection without knowing target accuracy**.\n\n| Method | UMS | C→L | C→S | C→V | L→C | L→S | L→V | S→C | S→L | S→V | V→C | V→L | V→S | Avg |\n|--------|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|\n| No adapt | ✗ | 47.8 | 53.3 | 64.6 | 51.3 | 40.7 | 55.1 | 58.1 | 36.4 | 55.4 | 97.8 | 48.8 | 72.0 | 56.7 |\n| SHOT [28] | ✗ | 44.6 | 55.7 | 75.9 | 65.3 | 60.8 | 74.8 | 87.7 | 41.1 | 82.7 | 89.8 | 45.8 | 65.7 | 65.6 |\n| SHOT++ [30] | ✗ | 41.7 | 58.4 | 75.6 | 70.0 | 56.9 | 76.2 | 71.6 | 39.5 | 80.7 | 96.9 | 43.7 | 60.07 | 64.3 |\n| AaD [59] | ✗ | 37.7 | 57.2 | 75.5 | 59.5 | 48.8 | 67.5 | 84.0 | 34.8 | 72.8 | 43.4 | 40.2 | 54.8 | 56.4 |\n| CoWA-JMDS [23] | ✗ | 46.1 | 58.4 | 81.1 | 85.6 | 64.1 | 78.2 | 95.9 | 48.1 | 82.7 | 99.4 | 50.8 | 65.3 | 71.3 |\n| NRC [37] | ✗ | 39.9 | 55.9 | 75.5 | 64.7 | 54.1 | 74.8 | 78.4 | 40.3 | 82.2 | 90.1 | 41.7 | 62.8 | 63.4 |\n| G-SFDA [58] | ✗ | 42.6 | 54.9 | 73.5 | 82.3 | 51.4 | 72.0 | 74.6 | 45.8 | 82.7 | 88.9 | 49.1 | 64.3 | 65.2 |\n| FT | ✗ | 50.1 | 66.7 | 81.1 | 99.7 | 62.2 | 78.0 | 99.8 | 55.5 | 80.5 | 99.7 | 53.0 | 67.0 | 74.5 |\n| LP-FT | ✗ | 50.5 | 66.7 | 79.5 | 99.7 | 65.6 | 77.6 | 99.7 | 54.1 | 79.5 | 99.7 | 51.2 | 69.8 | 74.5 |\n| **Ours** | ✓ | 88.2 | 96.0 | 90.8 | 97.7 | 97.2 | 99.2 | 99.6 | 88.6 | 89.4 | 99.9 | 92.7 | 91.5 | 94.2 |\n\n[1]Cui S et al. Towards discriminability and diversity: Batch nuclear-norm maximization under label insufficient situations, CVPR 2020.\n[2]Cui S et al. Fast batch nuclear-norm maximization and minimization for robust domain adaptation. arXiv:2107.06154.\n[3]Lee S et al. Few-shot fine-tuning is all you need for source-free domain adaptation. arXiv:2304.00792.\n\n### Q2：$E$ sensitivity. \nAs mentioned above, we uniformly set $E=0.01$ for all experiments across datasets. The ablation results with $E=0.001$ are only provided to demonstrate that a higher $E$ yields better performance, and this setting ($E=0.001$) was never actually used in Tables 1–3.\n\n### Q3: Selectively chosen of baselines in the performance comparisons.\nIn the ProDe and DIFO papers, ProDe-V and DIFO-C-B32 indicate direct use of CLIP-ViT as the backbone for the SFDA model. Nevertheless, most SFDA methods, including ours, use vanilla ResNet as the backbone. For fair comparison, we did not include ProDe-V and DIFO-C-B32. We have clearly specified the ResNet backbone in the captions of Tables 1-3.\n\n### Q4: Quantitative component-wise analysis. \n\nAblation studies are performed on each component in Response to Q1 by Reviewer fShy, and demonstrate the effectiveness of each component.\n\n### Q5: Low-frequency information remains in diffusion latents.\nYes. Prior studies [4][5][6] have demonstrated this conclusion, and they also operated frequency decomposition in the diffusion latent space. \n\n### Q6: Reuse already manipulated samples.\nThe target model determines which samples are included in the non-trust set, and usually selects those it struggles to classify. After training on their manipulated version, some non-trust samples become well-understood, as implied by the growing trust set. Therefore, retaining their manipulated versions is unnecessary and introduces two issues:\n* It creates an ever-growing synthetic domain which is much larger than the real target domain, causing the model to adapt to the synthetic domain rather than the real target domain.\n* It significantly increases training costs.\nWe offer a similar experimental analysis in Response to Q4 by Reviewer pUPg.\n\n[4]Everaert et al. Exploiting the signal-leak bias in diffusion models. WACV 2024.\n\n[5]Wu et al. Freeinit: Bridging initialization gap in video diffusion models. ECCV 2024.\n\n[6]Koo G et al. Flexiedit: Frequency-aware latent refinement for enhanced non-rigid editing. ECCV 2024."}]

Author rebuttal:

Meta-review: The paper proposes a source-free domain adaptation (SFDA) method called diffusion-driven progressive target manipulation (DPTM) that builds a pseudo-target domain instead of synthesizing a pseudo-source. DPMT is validated empirically on the Office-31, Office-Home, VisDA, and DomainNet-126 image classification datasets and can outperform the SOTA. Five experts in the field reviewed this paper. Their post-rebuttal recommendations are 3 Borderline Accepts and 2 Accepts. Reviewers appreciated the technical novelty of DPTM, the clear presentation of manipulation components, and the convincing empirical results. 

The authors provided a detailed rebuttal in response to reviews. The discussions and rebuttal clarified several concerns on class conditioning, computational complexity, fixed hyperparameters, ablations of individual modules, and comparisons to related diffusion-based SFDA approaches. These concerns were well addressed in the rebuttal, and three of the reviewers raised their ratings. Although remaining concerns remain, mostly to computational cost, and the need for more qualitative visualizations and comparisons with SOTA methods, the strengths outweigh the weaknesses. These were partly addressed in the rebuttal. Overall, this paper makes a decent contribution to the SFDA literature. I therefore recommend acceptance as a poster. The authors are encouraged to make the necessary changes to the best of their ability.
Decision: Accept (poster)