VirDA: Reusing Backbone for Unsupervised Domain Adaptation with Visual Reprogramming
Authors: Duc-Duy Nguyen
Dat Nguyen
Venue: Accepted by TMLR ()

Abstract: Image classification is among the pillars of computer-vision pipelines. While state-of-the-art models excel within their training domains, their performance often deteriorates when transferred to a new, unlabeled setting. Unsupervised domain adaptation (UDA) addresses this challenge by repurposing a well-trained source classifier for the target domain, enabling strong downstream results without the need for additional labeled data. Existing UDA pipelines fine-tune already well-trained backbone parameters for every new source-and-target pair, resulting in the number of training parameters and storage memory growing linearly with each new pair, and also preventing the reuse of these well-trained backbone parameters.

Inspired by recent implications that existing backbones have textural biases, we propose making use of domain-specific textural bias for domain adaptation via visual reprogramming, namely VirDA.
Instead of fine-tuning the full backbone, VirDA prepends a domain-specific visual reprogramming layer to the backbone. This layer produces visual prompts that act as an added textural bias to the input image, adapting its ``style'' to a target domain. To optimize these visual reprogramming layers, we use multiple objective functions that optimize the intra- and inter-domain distribution differences when domain-adapting visual prompts are applied. This process does not require modifying the backbone parameters, allowing the same backbone to be reused across different domains. 

We evaluate VirDA on Office-31 and obtain 92.8% mean accuracy with only 1.5M trainable parameters. VirDA surpasses PDA, the state-of-the-art parameter-efficient UDA baseline, by +1.6% accuracy while using just 46% of its parameters. Compared with full-backbone fine-tuning, VirDA outperforms CDTrans and FixBi by +0.2% and +1.4%, respectively, while requiring only 1.7% and 2.8% of their trainable parameters. Relative to the strongest current methods (PMTrans and TVT), VirDA uses ~1.7% of their parameters and trades off only 2.2% and 1.1% accuracy, respectively.

Published in Transactions on Machine Learning Research (12/2025)
VirDA: Reusing Backbone for Unsupervised Domain Adap- tation with Visual Reprogramming
Duy Nguyen
Hanoi University of Science and Technology
b0g.nd223495@huss.coo edu. un
Dat Nguyen
Haroard University
datnguuyenBisens.kaaa www.ssseeesssaaadd....o. edu
Basis.ai
dat@basss.co
Reviewed on OpenReview: https: // openreview. net/ net/forum? ? id= QhJF77FFF1
Abstract
Image classification among the pillars of computer-vision pipelines. While state-of-the- art models excel within their training domains, their performance often deteriorates when transferred to new, unlabeled sctting. Unsupervised domain adaptation (UDA) addresses this challenge by repurposing a well-trained source classifier for the target domain, enabling strong downstream results without the need for additional labeled data. Existing UDA pipelines fine-tune already well-trained backbone parameters for every new source-and-target pair, resulting in the number of training paramcters and storage memory growing lincarly with each new pair, and also preventing the reuse of these well-trained backbone parameters.
Inspired by recent implications that existing backbones have textural biases, we propose making use of domain-specific textural bias for domain adaptation via visual reprogramming, namely VIRDA. Instead of fine-tuning the full backbone, VIRDA prepends domain-specific visual reprogramming layer to the backbone. This layer produces visual prompts that act as an added textural bias to the input image, adapting its "style" to a target domain. To optimize these visual reprogramming layers, we use multiple objective functions that optimize the intra- and inter-domain distribution differences when domain-adapting visual prompts are applied. This process does not require modifying the backbone parameters, allowing the same backbone be reused across different domains.
We evaluate VIRDA on Office-31 and obtain 92.8% mean accuracy with only 1.5M trainable parameters. VIRDA surpasses PDA, the srate-of-the-ars parfieter-efficient UDA bascline, by +1.6% accuracy while using just 46% of its parameters. Compared with full-backbone fine-tuning, VIRDA outperforms CDTrans and FixBi by +0.2% and +1.4%, respectively, while requiring only 1.7% and 2.8% of their trainable parameters. Relative to the strongest current methods (PMTrran and TVT), VIRDA uses 1.7% their parameters and trades off only 2.2% and 1.1% accuracy, respectively
Introduction
Recent advancements image classification have significantly enhanced model performance through super- vised lcarning, driven primarily by large amounts of labelod data. (He al.) 2016 Dosovitskiy al. 2020; Liu et al.] 2021 Han et. al. 2022). However, these supervised approaches struggle when applied to new, unlabeled domains due to domain shifts (Ganin & Lempitsky 2015). This challenge is particularly promi- nent in fields involving emerging technologies, such as medical imaging for newly discovered discases, where
acquiring labeled data is costly and lime-consuming (Abedi et. al. 2024
We release our implementation and reproduction package htap:///github.com//iii Mguyeu---ynnn--D/VViraaa Ducc/iirDDD

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Thrget
03.72%
PatchMix
(96)
SSRT
*virDA (Ours)
Ostta
Bodic
CDTrans
(b) Reprogaammied Target image
FixBi
PDA
91
SCDA
40-
MaPLe
DCAN
VI0A (Durs)
90,57>>
Parato fiobbar
80
Training Parameters (Millions)
Bk:
Ouite Bosic
(a) The Pareto chart of existing methods, with VIRDA displaying the trade-off between accuracy and the num- ber of training parameters.
(b) Classification result of a well-trained model on the source domain enhances when applied to the target do- main, due to using pixel-wise textural mask.
Figure 1: Notably, our method excels over other parameter-effiient fine-tuning methods using CLIP as the backbone (e.g., PDA and MaPLe), well other methods that require full fine-tuning (e.g., FixBi and CDTraan)) at minimal computation cost. Moreover, VIRDA required only 1.7% training parameters (1.5M to 86.6M) while sacrificing 2.2% accuracy compared to the SoTA method.
To address this limitation, Unsupervised Domain Adaptation (UDA) (Ganin & Lempitsky 2015 Ganin al., 2016 Saito et al. 2018), aims to adapt well-trained source domain classifiers to the target domains, given that labels of the target domain are available. Prior UDA methods aim transfer well-learned representations, the hidden features that are sharcable between the source and target domains, and are invariant to the domain-specific style (e-g., differences in studio-lighting versus practical lighting condition (Venkateswara
ctt al.
2017], coloring style between synthetic versus real-world imagery (Saenko et al. 2010 Existing
works ZZhang et al.] 2019: Yang et al., 2023 Na et al. 2021) facilitate this transfer by adapting the hidden
features produced by the backbone in a typical image classification framework (Deng et al. 2009 Krizhevsky
et al.
2012) through various means, e-g., feature distribution alignment. (Chen et al.) 2019a b Sun et al.
2017]
and adversarial training (Long et al. 2018 Ganin & Lempitsky 2015). Others utilize the pretrained
Vision-Languee models to use the common lextual label set to guide the adaptation
GG el. al. 2022 Du
et al.
2024 Bai et al. )024).
While these methods work well and achieve high accuracics, the transfer requires finc-tuning both the well- trained classifier along with fine-tuning the backbone, either a convolutional neural network (CNNs) (Long) et 2018: Ganin & LLmpitsky 2015) or Vision Transformer backbones (Yang al. 2023 Zhu et al.,
2023
Xu et al.) 2022 Liang al. 2020), for each new domain. This limits backbone reusability, as well
requiring large amount of storage to store the trained backbone across different source-target domain pairs.
In this paper, we propose Visually Reprogrammed Domain Adaptation (VIRDA), which aims to facilitate backbone reuses through a lightweight visual reprogramming layer. Recent findings show that even well- trained backbones that produce robust features still have textural bias, making them overly reliant on textural patterns for each domain and each category inside the domain @Geirhos al.) 2018 We thus exploit these textural biases for domain adaptation through visual reprogramming (Cai et al.) 2024b): each visual reprogramming layer consists of domain-specific textural pattern that aims to capture the domain- specific textural bias, and a per-instance mask generator that adaptively applics this textural pattern over the input image. Applying this visual reprogramming layer is thus equivalent to shifting the style of the input image (either from the source or the target domain) towards a common style, learned by the backbone (Figure 1b for an illustration). More specifically, this visual reprogramming is prepended to the backbone and thus does not require backbone modification or fine-tuning. Thus, our resulting architecture for each domain is composed of three modules in a cascaded manner: (1) domain-speeific visual reprogramming
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layer, (2) frozen, reusable backbone, and (3) domain-specific classifier. To perform UDA and to train these visual-reprogrammiigg layers and domain-specific classifiers, we design two key objectives: (1) an inter- domain alignment objective that aligns the learned hidden features and classification uncertainty from both the source and target domains, and (2) an intra-domain alignment objective that aims to learn domain- specific Teatures through self-supervised loss.
We conduct experiments evaluate VIRDA's capability in classification effectiveness, training parameter size, and storage requirement for each source and target domain pair. Our experiments demonstrate that the proposed VIRDA, requiring only maximum of 1.5 million of training parameters (less than 2% of PMTrans Zhu et al. 2023)) and only 6 MB for storage per domain (compared to over 340 MB of both PMTrrnn and CDTrrans (Xu et al. 2022)), ), and fully reusing the backbone's parameters, achieves comparable performance to state-of oatie---ffhh--rttt the-art (SOTA) methods across standard domain adaptation benchmarks, including
Office-31 (Saenko al.) 2010), Office-Home /Venkateswara et al. 2017], ) and Digits (MNIST (LeCun et al. 1998), USPS (Netzer ct 2011 SVHN Hull 1994)). The main contributions of this paper are summarizod follows.
We propose a novel UDA method that efficiently addresses domain shifts by exploiting inherent textural biases in pretrained models, enabling lightweight yet effective domain adaptation.
To the best of our knowledge, we are the first perform domain adaptation with an entire single- modality frozen backbone by integrating visual reprogramming within the framework.
We evaluate our method on three widely used benchmarks, confirming the effectiveness of our method with only fraction of the training parameters, while achieving competitive performance compared to existing methods (as shown Figure lay.
The remaining of this paper is structured as follows: presents the detailed architecture and methodology of VIRDA; Section provides comprehensive experi- mental results and extensive ablation studies; and finally, Section summarizes our conclusion as well as future research directions.
Scction 2
reviews related works to ours; Section 3
2 Related Works
In this section, we summarize three related research directions to VIRDA, namely, unsupervised domain adaptation, parameter-effiiien fine-tuning for domain adaptation, and visual reprogramming.
Unsupervised Domain Adaptation As introduced in Scction [ existing UDA mothods aim to align the well-learned hidden representation of the backbone's output. Long et al., 2015 and (Long el al., 2017) proposes Deep Adaptation Network (DAN) and Joint Adaptation Network (JAN) that align different task-specific hidden representations by aligning their embedding distances with Maximum Mean Discrepancy (MMD), (Wen et al. 2219)) instead uses uncertainty matching to align hidden features. (Ganin & Lempitsky 2015), on the oiher hand, performs feature alignments through the use of inverse gradient from an adversarial domain discriminator, aiming to make the source and target features indistinguishabbl. (Long et al.) 2018)
improves per-class feature representation by clustering hidden features for adversarial training. (Tzeng et al.
2017 combines both feature distribution aligument and adversarial training. (Saito et al. 2018) and (Li
ct al. 2020) aim to further improve the precision of feature alignment using the classifier's disagreement
pinpoint and domain-specific attention modules (Saito al.] 2018 Li et al. 2020). Other works employ self-ensembling frameworks, improving consistency of predictions under perturbations with contrastive losses and self-supervised losses (Cui et al. 2020b Yue et al. 2021 Xu et al. 2022 leveraging temporal smoothing to stabilize representations (Tarvainen & Valpola 2017). and adapting Batch Nuclcar-norm Maximization on the output matrix to improve prediction results (Cui et al. 2020a). Finally, recent methods combine both inter- and intra-domain alignment strategies (Na et al. 2021 Yang et al., 2023). We adapt these alignment strategies, specifically, we perform inter-domain alignments on the visual reprogramming layers with uncertainty matching and adversarial training, as well as intra-domain alignment with self-supervised loss and consistency objectives.
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Parameter-cffiiiett fine-tuning (PEFT) for domain adaptation As alternative fully fine-tuning the backbone, recent PEFT approaches leverage large vision-language models' multimodal inference capabil- ity and instead learn a small number of prompt or fine-tuning image-text adapter modules. MaPLe (Khattak] ctt al. 2023) injects and finetunes a small set of context prompting tokens for each text-encoding layer to better align the hidden representation of domain-specific visual and lext-based tokens. DAPL (Ge et al. 2022 also aims to modify CLIP for UDA, however, they finetune class-aware and domain-aware textual prompts with pseudo-labels. PDA (Bai al.) 2024) aims better learn cross-domain shift by combining learned prompts with lightweight image-guided feature tuning branch that performs distribution alignment under pseudo-labels. DAMP (Du et al. 2024) pushed the idea further by replacing image-guided feature tuning with a more powerful multi-domain transformer decoder. Our method can also be seen as fine-tuning "prompts" as a means to perform UDA. However, instead of learning a lextual prompt that applies to large pretrained multimodal model, we apply the visual prompt to highlight that domain adaptation is feasible via lightweight visual reprogramming of the input hattt/paacc-embedding space, where small set of learnable image-side tokens steers (mostly) frozen backbone-achievinn tation. Because approach does not make the assumption of using a multi-modal rision-language model, it can be applied to any of the existing backbones, i.e., both convolutional neural fedwork-based backbone or vision transformer- basee backbone.
parameler-effiienl,, backbone-agnostic adap-
Visual Reprogramming Visual Reprogramming (VR) is a method that repurposes pretrained vision back- bones by learning small input-side modifications (e.g., additive prompts or adversarial "programs") so that the fixed or lightly adapted model performs a new downstream task without full retraining (Cai et all 2024b). Recent works in VR focusing on learning perturbations guided by descriptive and distinctive at- tributes to improve alignment in Vision-Language Model (Cai ct al. 2025
incorporating adversarial However, these methods are only applicable to supervised training, and leave a research gap when applied to the unsupervised char- acteristics UDA. Other visual prompting techniques have been applied to UDA, for example, in image classification (Gao et al., 2022), or image segmentation (Ma et al. 2023), employing visual prompting inside transformer architectures by inserting prompts into intermediate layers facilitate cross-domain alignment. While VIRDA also uses visual reprogramming for UDA, we aim to leave the backbone unmodified to fa- cilitate its reuse; instead, rely the aforementioned domain alignment objectives to train the visual reprogramming layers.
examples to improve the robustness of re-programmed models (Zhou et al. 2025
(a) Inter-Domain Alignment
(b) Mitra-Domain Alignment
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Figure 2: The overall pipeline of VIRDA.
3 Methodology
The unsupervised domain adaptation (UDA) problem for image classification (aani & Lempitsky 2015) takes input two datasets: labeled dataset D3- {(x!") (s) (,))) from source domain S along with
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unlabeled target domain 7's datasel Dr Dr= (x j== where each x(*) and x(1) is the input data samples (i.e., images) from the source and target domain respectively, and y(s) is the sample label (i.e., image labels).
We denote the mini-batch of input images be B(*) (xf") xx(] :.... ;...,,; x(()) and B(O {x[² xx ..... "=""""....,. ) y.
for the source and target domains, respectively, with the y( = {y] .22 ......kk') be the corresponding source labels. Although the unsupervised setting of the target domain T, we assume that the two domains share an identical label space y. As previously described, VIRDA consists of a visual reprogramming layer that produces visual prompts describing textural and spatial shifts between different domains. We describe this representation briefly in Section 3.1
To optimize this visual reprogramming layer, we follow the formulation of existing works and attempt to align the hidden features produced by applying these layers in Section 3.2 (See Figure 2] These hidden features are aligned intra-domain with domain-specific data augmentations, and inter-domain by aligning hidden features produced with different domain-specific visual reprogramming layers. Specifically, the inter-domain alignment loss implemented using three sub-loss functions: the supervised source loss Leup: the adversarial loss that aims to align inter-domain feature alignment Ladv; and Lune that aligns between the class-wiss prediction uncertainty of source-and-targee domains. The intra-domain loss, meanwhile, constructed using two sub-loss functions: (1) Lunsup, an unsupervised consistency loss enforcing the same image under two different augmentations yields the same output, and (2) confidence-distribttion matching loss Ldistrib that encourages the output confidence distributions from differently augmented views of the same image to be similar.
3.1 Encoding Domain-specific Textural and Transformational Visual Prompt
Visual Prompt Representation Assuming the input image is represented tensor x E Ruxxxx where is the number of color channels (usually 3), a visual reprogramming module is a pair (t, fmask), where E R=xxxe is the domain-specific textural pattern, whilc fmask Ruxhxe + [0,1]wxxxc is the mask producer.
Visual mask- producioo Layer mask maok--roouucing layer fmask is a function that takes as input an image and produces the mask. The function is a fully convolutional subnetwork of Ler layers, where cach layer E (1,..., ]er performs 3 x 3 convolution with padding of 1. The feature map is then downsampled into non-overlapping patches of size Nor 22", allowing the network to learn a compact summary of local texture and shape cues within each patch. In our experiments, we follow Cai et al. (2024b) to set Lor E (5, 6}, while Nor E [1, 5]. While deeper reprogramming layers with larger patches excel at capturing coarse, object-level patterns, shallower layers with smaller patches preserve finer, more detailed features.
Structural mrok-prrouuiin layer. Although a visual reprogramming layer can mask visual appearance shifts, the cross-domain mismatches are also displayed in spatial layout and feature dependency, so that the downstream model still learns the wrong structural priors. For example, product images often center the object, whereas real-world images place it off-center. To model this structural shift, we adopt the Coordinate Allention (CA) mechanism of Hou el al. (2021) to generate two axis-wise, position-sensitive masks that softly recenter attention. CA first aggregates features along one spatial axis at a time to preserve positional information along the other axis:
1 h E E (1.j.c) = + T
gh (n
1,
1 w
Σ x(i.j.c) e xxxxe
(1)
The two aggregated tensors are concatenated and passed through a shared 1 x convolution:
z = CCovvixx([B,.. Bw]) E (f
(2)
We then split z into n₁ E p and zu E R lxxxx, and transform each branch with its own 1 1x convolution followed by a sigmoid gate a to obtain the axis-wise attention masks:
Ap (Conv (N) (2x)) (2h))) E hxxxx
Aw (Co (w) (z))) TR 1xxxx
(3)
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Broadcasting An and Aw over the missing dimension yields our position-shifting masks (vertical and hor- isontal, respectively). Applying them reweights rows and columns-analogous to softly shifting emphasis toward locations that are more likely in the target domain:
fenora (x) C An 3 Aw
(4)
where is the clement-wise multiplication operation. We optimize the parameters of this structural layer jointly with the visual reprogramming objectives described in Sec. 3.2
Visual reprogramming layer The visual re-programming layer Sare works as follows:
[pre(x)= = [x)ora(x) + t Smask (Seoor(
(5)
The textural pattern t and both the mask proouiig layers fmask feoord are domain-specifie. Hence, for each domain ts we denote t(a), f(d) and f(d) for domain d's pattern and mask-produuing layers, and fpre as the visual reprogramming layer for domain d.
Intuitively, each visual prompt is a combination of structural masking and textural reprogramming. Hypo- thetically, the structural mask, i..., the successive application of fcoord and smask. highlights the regions to be re-programmed for each image, while the textural bias should capture the domain-specific style that the backbone has biases towards, following the finding of the existing work (Geirhos et al. 2018).
3.2 Visually-aligning Model
Unlike previous visual reprogramming works (Cai et al. 2024b Zhou et al. 2025 Cai et al. 2025 that assume the availability of target-domain labels, our approach must function in an unsupervised sctting. Con- sequently, our classifier architect requires sufficient flexibility to leverage both shared (domain-invvrrann)) and unique (domain-speciffc) features effectively (Xiao et al.) 2021). To achieve this, we introduce a visual reprogramming module that explicitly couples domain-invariant features, combined with loosely coupled, domain-specific classifier heads that capture specialized features.
Full model architecture Thus, we design an architecture that couples the common features through vistaal-repoogrammiig module, while also enabling learning to make use of domain-specific features, through lowly-coupled domain-specific classifiers. Following Eq. 5 we denote the domain-specific visual- reprogramming module for the source domain and the target domain t as fire and fpre, respectively. We also denote the domain-specific classifiers for the source and target domains as f(" and f(") respectively. Thus, our model architecture as depicted in Figure [ for each domain, we cascade three modules: a domain-specific rissallreprogramming module fpre. reused comain-invariant backbone foackbores and fi- nally domain-specific classifier fc. To train this architecture, we have to model two goals: (1) we have to perform inter-domain alignment, and (2) we have to perform intra-domain alignment.
Inter-domain alignment Since we do not re-train our backbone, as such, nnter-domain alignment implies different objectives for the visual reprogramming modules and the domain-specific classifier, respectively. For the source and target visual re-programming modules p(t) and ((t) this alignment means that these modules have to be able to "shift." the input image style from their respective domain to that of a common image style that was learned by the shared backbone. For the domsin-specific classifiers ((a) and f( this means that they have to learn to use the shared or aligned inter-domain features. Concretely, the inter-domain loss is implemented via three objectives:
Linter = Lsup + Ladv + Lunc
(6)
To keep the source classifier well-trained during adaplation, which is crucial to transfer the feature-label information when f learned the common style, we first implement supervised loss on the source data:
Cxup 1 k
Σ 1:
CEly! .ppp))))))))) (
(7)
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where pi - (2, and zi (d) (Jbackhane Spre) (x; (0)) is the prediction and features obtained for each sample in the domain d's batch B(4), respectively. The Lade is the adversarial domain discriminator loss, used to help the risuallroprogramminng modules to shift their respective domain images into a common domain and produce indistinguishabble hidden features. This loss is modeled via. binary cross-entropy over the predictions produced by domain-specific discriminator fdomain. Hypothetically, both the source- and target- visual reprogrammingg modules will produce aligned hidden features that can "fool" the domain discriminator fdomain, defined as:
Lado
log Idomain (2(²)) + log - fdomain(2)) (2()))
(8)
k
If the domain discriminator can still distinguish the produced features, then the backpropagated signals will be used to train both fdomain and the risual-reprorramming modules through reverse gradient (Ganin & Lempitsky 2015). Given that the source and target domain hidden features are aligned, the source classifier (6 should be generalized both domains to leverage the use of labels. This means that can both learn robust domain-invariant features produced by the target visual re-programming module fpre, while remaining well-trained on source domain inputs. Following prior work (Wen et al. 2019), we propose to use the uncertainty loss Lune- This objective implemented align f(''s uncertainty in different domains, i.., the distribution of f(')ss uncertainty should be the same on both the source and target-reprogrammeed domain. Indirectly, this objective will also enforce nomer-domain aligmment between two visual-reprogrammiing modules. Recall that we have a batch of input images from both domains, B(s) and B(O) We perform M stochastic forward passes of the full modulc and sample the per-instance, por-class uncortainty on both the reprogrammed source batch B(s) and the reprogramuned largel batch B("), For each instance and class c, we collect the M predictive samples (Pm,e(y x,) and treat them as draws from per-instance, per-class uncertainty distribution. We use these point-wise uncertainty estimation to estimate the full uncertainty dist ristbbutions gfo)) and a(0) We then align uncertainties by minimizing the class-averaged KT. divergence:
Lune
KI 1 1..))
(9)
1D
i=1 eEy
This encourages matched predictive uncertainty across domains and indirectly aligns the two visual- reprogramming modules. In practice, we place Dropout layers with different probabilities Pmask and po in both fio and f(", respectively to measure uncertainty (Gal & Ghahramani 2016).
Intra-domain alignment To enhance the performance of our model on the target domain, we employ intra-domain objectives to transfer the already-well- treinet source classifier f(")'s classification capability to the target domain classifier ,(0, while also allowing the target domain classifier to learn intra-domain robust feature. Specifically, the intra-domain alignment loss is:
Lintra = Cunsup Cdistrib
(10)
where both of our objectives enforce consistency through different views of sample. This is done by augmenting the target domain image through both strong augmentations, such as affine transformation color jitter, and weak augmentations, which are the normalization of original image. The resulting augmented images' hidden features are then passed through both the source classifier ((ر) and the target classifier S(P Assuming the features are well aligned to certain level through prior objectives, we use f(() to produce pscudo labels for the target classifier p(t) The Lunsup thus to optimize f(() to minimize both the difference in prediction of f(() and f((), well as reducing f(')'s uncertainty over augmented target images while the Laistrib penalises any disagreement between the larget classifier p(( and the source classifier ((0), More concretely, let fweek denote the weak augmentation and fetrong denote the strong augmentation. From (1) B(), obtain the weak-view prediction Piweak = (1(2) Sbackbone f o smeaak) (x(")) and the strong-view
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(8) prediction Pi,strong (/e Jbackbone
(0) sstrong) (xx
(x+
Thus, the distribution divergence loss is:
Ediatrib
KKL (KL wiewe Pi,strong)
(0)
(11)
We use the most confident predictions from the weak augmentation branch for each sample, (()) arg max Pia to construct our unsupervised loss:
1 h Σ h 1=1
Cunsup
CE((E 10
Pistrong))
(12)
3.3 Training objectives and inference
To train our model using the five losses, the training objectives can be defined as:
Oune Lune + andv Ladv + aintra Lintra
(13)
At inference time, we perform on the target domain using both the target visual reprogramming layer and the target classifier for label prediction:
0. arg max (5e) Siackbone S (z:)),
(14)
where i is the predicted class of the unlabeled target sample.
4 Experiments
We evaluate proposed method widely used three domain adaptation benchmarks, namely Office-31, Office-Home, and Digits, compared with state-of-the-art UDA methods in both accuracy and number of training parameters. In addition, we validate the contributions of the proposed method through extensive ablation studies. We describe detailed dataset characteristics and implementation details below.
4.1 Datasets
Digits is dataset composed from three other digit datasets, which MNIST LeCun et al. 1998
USPS (Hull 1994), and Street View House Numbers (SVHN) (Netzer et al. 2011 In terms of domain characteristics. MNIST (M) contains grayscale digit images with a clean background; SVHN (S) consists of cropped coloured digits from real scenes with extremely blurred appearance; USPS (U) provides grayscale handwritten digit images with unconstrained writing styles. Whilst sharing the same 10 (0-9) digit classes, the three datasets present ed different data distributions, therefore suitable for UDA evaluation. For the UDA test,, we adopted three commonly used cross-dataset transfer settings with the standard data split: S-M, U-M, M-U.
Office-31 (Saenko et al. 2010 is the most popular dataset for real-world domain adaptation. It contains 4,110 images of 31 categories in three domains: Amazon (A), Webcam (W), DSLR (D). We evaluated all methods on six domain adaptation tasks.
Office-Home Home /Venkateswara al.) 2017) more challenging benchmark than Office-31. It consists of images of everyday objects organized into four domains: artistic images (Ar), CLIP art (CI), product images (Pr), and real-world images (Rw). It contains 15,500 images of 65 classes.
4.2 Implementation Details
In all experiments, we use both Resnet (He et al. 2016 and VIT (Dosovitskiy et al. 2020) models pre-trained ImageNet. (Deng et al. 2009) the fixed backbone for VIRDA. For the Digits tasks, we ResNet-18

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with a learning rate of 3 for the classifier heads and 5 for the visual reprogramming modules, using batch size of 128. The dropout rate for the classifier and the mask generator is set as Pmask = 0.5 and PC = 0.3, respectively. On the Office-Home and Office-31 datasets, we adopt ViT-B/32 as the backbone for all transfer tasks. We set the same learning rate as above, using a batch size of 32 and Pmosk = 0.3 with Pc = 0.1. For all experiments, we adopt AdamW (Loshchilov & Hutter 2017) with the default con configuration of (81,8)) (0.9, 0.999), and a weight decay of 1e-5. On the Office-31 and Office-Home datascts, we set Ler and Nor corresponding to 6 and 5, for coarse object-level mask, while on Digits we set Ler 5 and Nor 4, the dataset's characteristics demonstrate mild transformation. On all tasks, we set the number of forward passes to estimate uncertainty M = 4. We train our method in two phases, namely burn-in in 20 epochs and domain adaptation in 30 epochs for all data settings following Li et al. (2022). The scaling factor for each loss is set as Qune = 0.3, Ondv = 0.1 and Otintra = 0.15, in which these hyperparameters are obtained via grid-search. We also provide the reproduction package to reproduce this grid-search and training process. The experiment results reported here were obtained on a machine equipped with Intel Xcon Gold 6130 CPU at 2.10GHz clock speed with 16 cores and 64 GB of RAM running Linux and using a single NVIDIA GTX 3090 device. We also provide quantitative comparison in Table showing the differences in training, memory, and computational efficiency between our approach and full-backbone fine-tuning.
4.3 Results
To provide comparison, we compare VIRDA with the widely-rcoognized statc-of-thc-art mothods on Office- 31 and Office-Home datasets that use different. backbones. Specifically, we include MSTN (Xie el. al.) 2018)
DCAN Li et al. 2020), SCDA (Li et al. 2020), FixBi (Na et al. 2021 as baselines for the Resnet backbone;
baseba SSRT (Sun et al.
2022). PMTrans (Zhu et al. 2023), CDTrans (Xu et al.
2022), TVT (Yang
et al. 2023) as the baselines for the ViT backbone; DAMP (Du et al. 2024), PDA (Bai et al. 2024).
MaPLe (Khattak et al. 2023). DAPL, (Ge et al. 2022) as the baselines for parameter-efficiee
fine-tuning on
the CLIP backbone. These include a wide range of backbones, especially, with CLIP, VIRDA a plugin to a backbonn that can already incorporate text features. We follow the setting of Ge et al. (2022) and apply
VR layer priorly to the image encoder to report our model with the CLIP backbone. Note that on all of our transfer tasks, we keep the backbone frozen, and we estimate the number of training parameters of our method on the visual prompt layers and the classifier heads.
Results on Digits. We display the performance of VIRDA on Digits tasks in Tab. [ where it achieves a mcan accuracy of 94.8% on the benchmark using only 0.2M training parameters, which is better than Cy- CADA (Hoffman et al. 2018) and GTA (Sankaranarayanan el al. 2018), while underperforming MCD (Saito et al. 2018) by a small margin. Compared to DANN, which uses the most parameters in our benchmarks, VIRDA delivers higher accuracy on all tasks. While MCD achieves a slightly higher mean (95.6%), VIRDA remains competitive across all shifts while operating with the smallest model size among the compared methods.
Table 1: Accuracy (%) on Digits for UDA (ResNet backbone). We highlight the best results in bold, and the second best results in underscore.
Method Training Params (M) M-U
U- M
S M
Mean
DANN
21.0
90.4
94.7
84.2
80.8
CyCADA
0.4
95.6
96.5
90.4
94.2
GTA
0.4
95.3
96.4
92.4
94.7
MCD
0.4
96.5
91.1
96.2
95.6
Ours (CNN)
0.2
95.4
95.9
93.0
94.8
Results on Office-31. As shown in Tab. 2 our method can improve the original backbonc's fine-tuning methods by 2% to 10% and outperforms some of the prior methods, such as MSTN and CDTrans, while only requiring a fraction of training parameters. VIRDA is capable of achieving over 99.0% on mild shift tasks D+W, while achieving strong results on tasks where the shift is hard but the source domain is visually more diverse than the target domain (such as A-W or A-D).. With the CLIP baselines, we improve the baseliee method DAPL by nearly 3% with the additional 0.5M training parameters, where on the mild shifts
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(A-W, A->D and W++ D) VIRDA consistently improves from 5% to 7%, while decreasing the accuracy harder shift. that requires semantic understanding, such as W-+ or D->A.
Table 2: Accuracy (%) on Office-31 for UDA with ResNet, ViT, and CLIP backbones. We highlight the best results in bold and the second best results in underscore.
Parameter size (M)
Training params (M)
Method
A- W D W
W-D A-D
D-A
W +
Mean
Resnet,50 baasee
23.8
23.8
68.6
96.8
99.3
69.1
62.8
61.0
76.1
MSTN
59.24
59.2
91.3
98.9
100.0
90.4
72.7
65.6
86.5
ResNet
DCAN
55.2
55.2
95.0
97.5
100.0
92.6
77.2
74.9
89.5
SCDA
24.0
94.2
98.7
95.2
76.2
90.0
FixBi
52.2
52.2
96.1
99.3
100.0
95.0
78.7
79.4
91.4
Ours (CNN)
25.6
2.1
84.9
96.2
100.0
90.2
64.2
66.7
83.7
ViT-basod
86.0
86.0
91.2
99.2
100.0
90.4
81.1
80.6
90.4
SSRT
86.0
86.0
97.7
99.2
100.0
98.6
83.5
82.2
93.5
CDTrrans
VIT
86.0
86.0
96.7
99.0
100.0
97.0
81.1
81.9
92.6
TVT
86.0
86.0
96.4
99.4
100.0
96.4
84.9
86.0
93.9
PMTrans
86.6
86.6
99.1
99.6
100.0
99.6
86.3
95.0
Ours (VIT)
87.6
1.5
94.7
99.0
99.5
97.9
81.3
84.1
92.8
CLIP-based (zero-shot)
124.0
75.8
75.8
77.7
77.7
79.0
79.0
77.5
PDA
153.0
3.2
92.1
98.1
99.8
91.2
83.5
82.5
91.2
CLIP
MaPLE
154.4
88.6
97.7
86.9
82.0
89.6
DAPL
124.3
0.3
80.3
81.8
81.8
81.3
81.2
81.0
81.2
Ours (DAPL)
125.1
0.8
85.3
89.3
87.3
85.7
77.1
79.0
84.0
Results Office- Home. VIRDA improves the baseline methods, such CLIP and Resnet, by minimum of 2%, while decreasing the accuracy when using the VIT backbones by nearly 1.5% with only fraction of training parameters ranging from 0.8M to 2.1M of training paramcters. With the VIT backbone, our method can improve and be on par with the hull-iinetning baseline by 0.8% to 2.9% on mild shifts (e.g., CI-Ar, Cl-Pr,, and Pr++Ar), while on the reverse shifts VIRDA constantly losing 3-5% in accuracy. However, the average accuracy of our methods when plugging in different backbones is still comparable with some of the prior methods, such MSTN CDTrans, while using only 1.7 3.5% of training parameters. Furthermore, when using the CLIP backbone, we outperform both the zero-shot CLIP and MaPTe in terms of accuracy, with only 0.8M of training parameters. We improve of 12 tasks, with 0.3-0.5% accuracy gain, while losing at most 1.0% accuracy in the other 5 tasks, resulting in slightly 0.1% mean accuracy overall. A detailed analysis of the behaviour of VIRDA is provided in Sec.
par with the bascline method DAPL on out
Table 3: Accuracy (%) on Office-Home for UDA with ResNet, ViT, and CLIP backbones. We highlight the best results in bold and the second best results in underscore.
Mathod
size (M)
params (M)
Pr Ar Rw
Pr-
23.5
23.8
$1.9
50
58
37.4
1.
46.2
35.5
31.2
60.4
53.9
41.2
50.0
16.1
MSIN
59.24
29.2
49.3
70.3
76.3
60.4
68.5
09.6
61.4
48.9
75.7
70.9
55.0
81.1
65.7
DCAN
RooNen
55.2
55.2
54.5
75.7
67.4
74.0
76.3
67.4
52.7
74.1
59.1
70.5
SUDA
24.0
65.7
74.0
74.5
ZULG
79.5
78.1
76.4
72.7
(CNN)
2.1
49.4
20.1
01.5
70.0
71.5
81.6
46.4
68.6
66.0
66.6
VIT based
86.0
56.0
67.0
85.7
58.
80.1
84.1
86.7
79.5
67.0
89.4
83.6
70.2
91.1
$1.1
SSRT
86.0
86.0
75.2
$9.0
91.1
85.1
88.3
80
65.0
744.2
91.2
85.7
78.6
91.8
85.4
CLVTbaES
86.0
86.0
08..5
$5.0
56.7
81.5
87.1
87.3
79.6
648.3
88.2
82.0
66.00
90.6
80.5
TVT
82.8
S3..3
74.6
S0.6
PAPT
(VTT)
1.6
85.7
80.9
87.0
61.4
80.8
63.6
70.6
CLIP hared (acto ebos)
124,0
0.0
67.6
89.0
82.4
80.0
82.4
67.6
80.
82.4
67.66
$3.1
PDA
153.0
3.2
23.5
91.4
91.3
86.0
91.6
Q1.5
96.0
23.6
91.2
88.4
23.0
90.1
$5.7
MaPLe
156.4
4.7
72.2
91.6
90.3
82.5
BIL.
89.5
92.4
7L.6
SHL.1
85.1
72.00
92.1
843
DAPL
126.5
0.3
20.7
91.0
00.9
85.2
91.00
90.9
85.1
70.7
90.8
65.3
70.4
91.4
84..
DAMP
(DAPT.)
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Product (Source)
Cllpart
Realworld
Produce (Source)
Cllpart
Realworid
Bepergram Driginal
Reprogg
Original Ing
Repregaan Ing
Regroggmm
ling
Regerggra
Postit Notes
0.00
37.
Sneakers
Clasevvée $6.0
Clas-vi
Flowers
Braser
accuncy::
accurssy:
Flipflops
accunscy
sccuaayy
Figure Samples and reprogram masks visualization classes using VIRDA transfer from the source domain (Product) the mild target domain (Realworld) and the hard target domain (Clipart). These classes chosen from the classes where our method performs worst and best in Clipart, indicated by the class-wise accuracy of VIRDA.
Table 4: Ablation Office-31 with VYT-B backbone finetuning. The best result in bold.
Finetuning layer
Training params (M)
D A
W
A W
Mean
Head
1.1
72.6
74.6
88.9
78.7
BB-Last
8.2
72.1
79.1
87.1
79.7
BB-Last-3
22.3
71.4
70.5
78.0
73.3
BB-1
8.2
78.7
80.3
87.1
82.0
VirDA
1.5
81.3
84.1
94.7
86.7
4.4 Ablation studies
Comparison with Partial Fine-Tuning While many UDA methods rely full fine-tuning of both the backbone and the classifier others achieve adaptation by updating only subset of parameters. For example, TENT Wang et al. (2021) adapts to distribution shifts by optimizing only channel-wise affine transformations at test time. To evaluate how VIRDA compares with these approaches, we conduct experiments on the three most challenging Office-31 lasks (D~>A, W-A, and A-W) using a VIT backbone Dosovitskiy al. (2020). All settings fine-tune the classifier head, with the following variants: (1) Head classitier head only, (2) BB-Last last backbone layer + head, (3) BB-Last-3 last threc backbone layers head, and (4) BB-1 first backbone layer head. Results presented in Tab. 4 The result indicates that VIRDA consistently outperforms partial backbone finetuning in terms of both classification accuracy by 3-5% and parameter efficiency (only requires 1.5M training parameters versus 8.2M training parameters from BB-1 and BB-Last.
Effect of Losses. Tab. illustaates how different combinations of loss functions affect the accuracy of A->D task the Office-31 dataset. While the normal combination of supervised loss and adversarial loss would be beneficial standard UDA training, for VirDA using this combination decreased the overall accuracy by -2%. We hypothesize that, while Lade enforces similar distribution of hidden features, for VirDA this translates to optimizing fmast and the textural fcatures, and also might forces the classificr head to learn more as optimizing the backbone becomes less flexible (since our later Error Analysis discussion indicated that the current fmask might be insufficient in some cases. This can result in overfitting or learning spurious features the classifier head. By adding more enforcing uncortainty loss, we force the classifier head also match the prediction uncertainty, thus minimizing the chance of overfitting. Introducing the inter-domain alignment loss, func: significantly improves accuracy to 93.8%, demonstrating its ivenessveness in narrowing
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the domain gap. Employing single intra-domain alignment signal, either Lunsup Ldiatrib, yields marginal improvements of +3.1% and +1.9%, respectively. However, combining both intra-domain alignment. losses enhances performance notably by +4.1%, resulting in the highest accuracy achieved in this task.
Table 5: Incremental accuracy gains for each additional loss term on A-D task on Office-31. The best
results are in bold.
Added Loss
Included Losses
Acc. (%)
Total Acc. (%)
Source-only
90.6
Suup
Lsup Crup + Ludv
+1.6
92.2
Lado
-2.
90.2
Lune
Deuy + Ludo + Lune (Linter)
+3.6
93.8
Lunsup
Linter + Lunsupp
4.3.1
96.9
Litistrih
Linter + Edistrib
+1.9
95.7
Lintra
Linter Lunsup Ldistrib (Cinter
Lintra)
14.1
97.9
Effect of Structural mask-producing layer. We evaluate the effectiveness of fecord on the same 3 tasks in Office-31 dataset in T With both backbones, which are ResNet50 and VIT, adding feoord yields a gain on all three tasks. This highlights that our coordination module scales especially well with transformer backbones, offering clear, consistent gains on the hardest transfers.
Source Data
12:08
Targel, Data
Figure 4: Visualization of the original image, the reprogrammed mask before (upper row) and after (lower row) UDA task on Rw-+r. The source domain masks focus on encoding the surrounding arcas, while the larget domain masks highlight the main object.
Efficiency metrics of VirDA. We report FLOPs, per-step training time, and peak GPU memory for VIRDA paired with different backbones in Tab. 7 VIRDA cuts training compute by 31-33% GFLOPs and shortens per-epoch time by =30% compared to full fine-tuning. Since the VR modules are placed before the frowen backbone, gradients must. still traverse the backbone to Irain these layers. Therefore, the memory usage yields different memory behavior across backbones: for ResNet, where peak usage is dominated by fcature maps, freezing changes little; for ViT, climinating attention-specifi weight-gradient buffers and optimizer state reduces memory by up 25%. In terms of inference computation efficiency, VIRDA introduced minimal over head of less than 5% increased GFLOPs.
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Table 6: Ablation on Office-31 with/withhuu the structural mask-producing layer fcoord. The best results are in bold.
Backbone
Scoord
D A
W-A A
A
ResNet50
63.2
62.3
83.1
ResNet50
64.2
66.7
84.9
ViT-B/32
X
74.1
81.4
90.9
ViT-B/32
81.3
84.1
94.7
Table 7: Efficiency metrics of VIRDA: FLOPs, training time, GPU memory usage. Here, Size indicate the input size, /GFLOPs and GFLOPs (Train) indicates the added GFLOPs from VIRDA and training GFLOPs accordingly, Training (s) indicates average required time per training step seconds and Mem- ory (GiB) indicates the memory usage.
Method
Size
1GFLOPs GFLOPs (Inference) GFLOPs (Train) Training (s)
Memory (GiB)
ResNet50 (Full)
21.9
0.59
12.6
0.4
ResNet50 (VirDA)
224
8.6
16.7
0.42
12.6
ViT-B/32 (Full)
384
25.3
77.2
0.53
24.1
1.2
ViT-B/32 (VirDA)
384
26.5
51.8
0.39
18.2
CLIP (VirDA)
224
0.4
1,180
1,180
0.53
12.6
Visualization. Figure visualizes reprogrammed masks before and after training source and target samples. Initially (upper rows), the masks exhibit diffuss, unclear patterns. Post-adaptation,, source masks emphasize background regions, while larget masks focus modifying primary objects. Masks effectively alter simpler objects like "Soda" or "Telephone", but face challenges with visually different objects like "Computer" or "Printer", and multiplo-object scenarios such as "Scissors".
Error analysis. Figure 3 visualizes how VIRDA transfers from the Product source two largets: a mild shift (Realworld) and hard shift (Clipart). The hardest failures in Clipart (e.g., "Post-it Notes', "Marker', "Eraser") arisc when the target removes the photo-like appearance cucs, shading, specular highlights, and fine material texture that the visual prompts leverage. The learned reprogram mask then attends to back- ground or non-discriminative regions; this can still reduce the inter-domain discrepancy loss, but does not support correct classification. In the easier Realworld target, where appearance is closer to the source, the prompts highlight object bodies (e.g., marker barrel, eraser odges/prints and accuracy improves substantially. Conversely, classes with distinctive, domain-invariant silhouettes (e.g., "Sneakers', "Flowers", "Flip-flops") transfer well both targets, even when texture simplifiod.
5 Conclusion and Future Work
In this paper, we propose novel method, VIRDA, a parameter-officient solution for UDA that's capable of reusing single pretrained backbone for all transfer settings. By introducing lightweight, domain-specific visual reprogramming layers that prepend to the frozen backbone, VIRDA adapts source knowledge to target domains through texture-level transformations rather than full network fine-tuning. We add intra- and inter-domain losses to guide the reprogramming function under the unsupervised constraint. Morcover, we leverage the prediction uncertainly to stabilize the training procedure. Our experiments demonsttate that VIRDA achieves competitive or superior performance compared to prior methods, and approaches the performance of statc-of-tho-art approaches while using only a fraction of the parameters. the future, we plan implement our VIRDA tackle the challenging downstream tasks, e.g., semantic segmentation and object detection.
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17


Reviews:
[{"review_id": "3pnz3pzDjZ", "reviewer": "Reviewer_kVZm", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "- The authors study the usage of visual reprogramming (VR) layers for unsupervised domain adaptation on the task of image classification. Changing the input with the VR layer, the backbone can be kept frozen and due to the authors it is then sufficient to only adapt the classifier (in contrast to adapt the entire backbone). The proposed framework is highlighted as being especially efficient in the number of updated/ learned parameters as the VR layer is relatively small compared to the Backbone.\n\n- 5 losses are assembled to learn the parameters of the VR layer and the classifier layer. \n\n\n- The authors evaluate their proposed framework on the Digits dataset with a ResNet backbone and on Office-31 and Office-Home dataset with a ViT-B 32 Architecture.", "claims_and_evidence_yn": "No", "claims_explanation": "In my understanding, the main claim is, that the usage of VR layer is especially parameter efficient while retaining a good performance in UDA. However, what is really lacking in my opinion, is an ablation comparing against different baselines using the same combination of the 5 losses:\n - No VR layer, only adapting the classification layer (as a baseline)\n - No VR layer but unfreezing different parts of the backbone (for example a few layers spread  throughout the network, only in the beginning or only in the end of the network).\n - For ViTs the literature for parameter efficient fine-tuning (PEFT) is very large (e.g. LoRA etc.). In my understanding many of these PEFT methods could be also used (learned with the loss), and serve as a baseline to compete with.  \n\nFor me the paper insists too much that for previous UDA method there is “only” a full-finetuning possible (“the transfer requires fine-tuning both the welltrained classifier along with fine-tuning the backbone”). However, keeping the backbone frozen and adapting only the classifier or adapting intermediate layers (e.g. like in TENT [1]) is also possible, and in my understanding many deep learning methods for UDA are quite unspecific about which weights in a network are updated.\nThe indeed interesting question if VR layer are better suited than e.g. unfreezing some layers of the backbone, deserves in my opinion a good portion of ablation experiments. \n\nBesides that, are Table 2 and Table 3 very difficult to interpret, mainly because different backbones are used, while the VirDA is only reported for a ViT (which has to be guessed by the parameter size). To keep the generality of the claim it is in my opinion important to verify the hypotheses for different backbones, especially as we see in Table 5, that parts of the framework work especially good with ViTs and maybe a bit less good with the ResNet. Also, comparisons across backbones are in my opinion always quite difficult, as the source-only performance can be quite different. \n\nI am also a bit puzzled about the usage of the 5 losses. Are these losses weighted or scaled ? (if yes, how were these weights/ scale factors set ?). Why is the adversarial loss kept, when we see in the ablation in Table 4, that it has a negative effect ? (does it have a positive effect with the “intra” loss ?) Is the assembling of the 5 different losses only working in the combination with the VR layer+ classifier update, or is this also working when fine-tuning the entire backbone. \n\nWhile the other concerns are in my opinion very crucial, I see the following one as a bit of a general concern with many UDA frameworks (and probably never perfectly solvable). The presented framework is evaluated on one modality (Images) and a single task (classification). While this is an important pair, the question can always be raised if the presented framework is specific to this or generalizes well to others. In my opinion it would make the paper much stronger, if there are also different tasks or modalities used for evaluation (e.g. semantic segmentation as a task, or point clouds as modality). Otherwise, I would make it at least clear in the claim, that it for now is only covering this single pair (see also my comment in the “minors” of the requested changes sections).   \n\n[1]  TENT: FULLY TEST-TIME ADAPTATION BY ENTROPY MINIMIZATION, ICLR 2021.", "audience_yn": "Yes", "audience_explanation": "The bigger the backbones get, probably the more people are interested in having to store only a small part of it to adapt it to different domains. Furthermore, it is computationally cheaper to train which is given the growing size of backbones, also an important aspect.", "requested_changes": "**Crucial:** \n\n- Comparing to different baselines where the loss is the same, but different parts of the backbone are unfrozen (early layers, layers spread across the network, later layers, classifier, or other PEFT strategies) \n\n- Clarification about the raised questions of the losses. \n\n- Reporting the performance of VirDA with a ResNet backbone/ CLIP backbone in Table 2 or Table 3. \n\n**Making the paper stronger:** \n- Experiment(s) with different modalities or tasks. \n\n**Minors:** \nl.1, Page 1: \n“Image classification is the foundation of nearly all computer-vision pipelines.”: \nWhile I agree on the importance of image classification, I would suggest to lower the tone a bit, as there are many other tasks: image recognition, semantic segmentation, instance segmentation etc. for also many different modalities e.g. image, video, point clouds. Therefore the “nearly all computer-vision pipelines” claim seems to be at least debatable. \n\n**Typos:** \nPage 9 : \nIn the paragraph “Results on Office-31” there is a typo: “only 1.5 parameters” → “only 1.5 M. parameters”\n\nSources: Several sources are linked with shortings. \n- Bai2024a and Bai2024b are referencing the same source\n\n- Du2024a and Du2024b are referencing the same source\n\n- Ge2022a and Ge2022b are referencing the same source (in Ge2022b is also a typo in the authors first name)\n\n- Yang2023a and Yang23b are referencing the same source\n\n- Zhu2023a and Zhu2023b are referencing the same source", "broader_impact_concerns": "None.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "EWebKlkeVg", "reviewer": "Reviewer_RVu7", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper proposes VirDA (Visually reprogrammed Domain Adaptation), a novel and parameter-efficient framework for unsupervised domain adaptation (UDA) in image classification. Instead of fine-tuning the entire backbone for each new domain, VirDA introduces a lightweight, domain-specific visual reprogramming layer that modifies the input images through learnable texture- and structure-aware visual prompts, enabling adaptation to new domains without altering backbone parameters. The method incorporates both inter-domain and intra-domain alignment objectives: the former aligns source and target domain features using supervised, adversarial, and uncertainty-based losses, while the latter refines the target classifier through pseudo-labeling and consistency regularization. \n\nThe proposal has been experimentally evalauted on multiple benchmarks (Digits, Office-31, and Office-Home). The strengths of this work lie in its high parameter efficiency, backbone reusability, and empirical results across different datasets and backbones. However, its weaknesses include limited analysis of accuracy that the propose method achieves, and the increased methodological complexity due to multiple loss functions and modules.", "claims_and_evidence_yn": "No", "claims_explanation": "While the paper presents interesting ideas and reports competitive accuracy, several claims are not fully supported by convincing or clearly justified evidence. First, although the authors emphasize parameter efficiency, Figure 1 suggests that both the forward and backward passes are still computed over the entire network, meaning that the savings in “trainable parameters” may not accurately reflect actual computational or training efficiency. Metrics such as FLOPs, training time, or memory usage would be more appropriate to substantiate these claims. Second, Table 1 shows a notable 10% performance drop on the S→M transfer without any explanation or analysis, raising concerns about the method’s robustness and consistency across domains. Third, the evaluation relies primarily on accuracy and trainable parameter count, but omits more informative measures of efficiency. Finally, certain results appear insufficiently justified—for example, in Table 3, DAPL achieves higher accuracy (70.7% vs. 61.4%) on Pr→Cl with far fewer parameters (0.3 M vs. 1.5 M), which undermines the claimed superiority of VirDA in efficiency–accuracy trade-off. Overall, while the results are promising, the evidence provided does not fully support the strength of the paper’s claims.", "audience_yn": "Yes", "audience_explanation": "The paper addresses a relevant and timely problem in computer vision—how to perform unsupervised domain adaptation efficiently without retraining large backbones for every new domain. Given the growing interest in parameter-efficient fine-tuning, visual prompting, and resource-efficient transfer learning, many readers of TMLR, especially those working on domain adaptation, transfer learning, and efficient model reuse, would likely find the core idea of visual reprogramming for UDA of interest.", "requested_changes": "1. The paper’s main motivation is parameter efficiency, yet Figure 1 and the method description imply that the forward and backward passes still traverse the entire backbone. The authors should clarify what computational savings are actually achieved (e.g., fewer trainable parameters vs. fewer FLOPs or reduced training time) and report additional metrics such as FLOPs, wall-clock training time, GPU memory usage, or energy cost. Without this, the claimed efficiency benefit remains unclear.\n\n2. Table 1 shows a ∼10 % drop in accuracy for the S → M transfer task, but the paper provides no explanation or ablation to analyze this behavior. A discussion of potential causes is needed to establish the robustness of the approach.\n\n3. Some results appear inconsistent with the efficiency–accuracy trade-off argument. For example, in Table 3, DAPL achieves higher accuracy on Pr → Cl with only 0.3 M parameters, whereas VirDA uses 1.5 M and performs worse. The paper should analyze why VirDA underperforms or whether differences in backbone settings or optimization explain the discrepancy.\n\n4. The training configuration and implementation details for the visual reprogramming layers (e.g., initialization of texture patterns, mask resolution, hyperparameters for the uncertainty loss) should be fully specified. Code or pseudocode would substantially improve clarity and reproducibility.", "broader_impact_concerns": "While the paper does not raise major ethical concerns, a few broader impact aspects should be discussed explicitly. The proposed method, VirDA, enables efficient reuse of pretrained vision backbones across domains, which could significantly lower the computational and environmental costs of model adaptation. However, by leveraging pretrained models and domain-specific texture reprogramming, the approach may also inherit or amplify existing biases present in the source backbone or datasets used during pretraining. Since the method adapts models without retraining or rebalancing underlying representations, biased visual priors could propagate to new domains, particularly in sensitive applications such as medical imaging or surveillance.\n\nAdditionally, the technique’s capacity to repurpose pretrained models across arbitrary visual domains raises potential misuse risks, such as adapting powerful backbones to privacy-invasive or discriminatory tasks without accountability. The paper should therefore include a short Broader Impact section acknowledging (1) the environmental benefit of reducing retraining cost, (2) the risk of bias propagation from reused backbones, and (3) the need for responsible application and dataset auditing when deploying VirDA in real-world settings.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "hUUlQemgCr", "reviewer": "Reviewer_KoCp", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper introduces a method for unsupervised domain adaptation (UDA), focusing on textural differences between images from the source domain and images from the target domain. The method learns a lightweight adapter that directly operates on input images. The adapter maps images into the source domain by applying coordinate attention and textural masking. Then, the image is encoded by a frozen backbone (for example, a trained ViT), and class labels are predicted by a domain-specific classifier. The adapter and classifier are optimized through a series of losses: A supervised classification loss on the source domain, a discriminator loss that encourages source and target classifier predictions to be indistinguishable, a loss that aligns uncertainty in classifications on the source and target domains, a divergence loss that aligns predictions on the target domain when applying image transformations, and finally a classification loss on the target domain using pseudo-labels obtained from the source domain classifier.\n\nThe proposed method is evaluated against several baselines on three tasks (digits, office-31, and office-home), all standard in UDA. Ablation studies further analyze the effect of different losses and the performance when using different backbones.", "claims_and_evidence_yn": "Yes", "claims_explanation": "Overall, the paper is technically sound and contains most information to understand the proposed method. In particular\n * The method is evaluated on standard UDA benchmarks\n * It is compared to an extensive set of baselines\n * Results are good, while not state-of-the-art. This is appropriately discussed in the context of contrasting parameter-efficiency and performance. While more expensive methods can achieve better performance, the paper makes a convincing argument regarding the memory efficiency of the proposed method, which could make it an interesting candidate in applications where memory is a concern\n * Ablations are relevant and insightful.\n * The visuals throughout the paper are helpful in understanding the method and results.\n\nRegarding the experimental setup, my main concern is that both digits and office-31 datasets are close to saturation, so it is difficult to assess the significance of any performance differences. This is not a point that needs to be fixed in this paper, but future work may consider switching to more challenging benchmarks.\n\nFinally, there are some issues with clarity which I will discuss below.", "audience_yn": "Yes", "audience_explanation": "UDA is a well-established field in computer vision, and therefore, many will be interested in developments within this field. The present paper is particularly focused on textural shifts and memory efficiency, both of which are relevant subfields with a significant range of applications, so not achieving state-of-the-art benchmark results is less of an issue.", "requested_changes": "There are main points that require changes:\n\n**(R1)** _Limitations_: While the focus on textural shifts and the memory-efficient scenario is implicit throughout the paper and mentioned occasionally, the paper would benefit from an explicit limitations section where the envisioned range of applications of the proposed method, and its respective advantages and disadvantages, are systematically discussed. This would help readers better contextualize the proposed method in the field of UDA and could inform future developments.\n\n**(R2)** _Clarity_: Because of the many architectural components and losses, the method description is hard to follow. Also, some details are omitted whose inclusion would facilitate understanding. I noticed the following points, which should be addressed in a revision:\n  * Motivation of mask in Sec. 3.1. From my understanding, the role of the mask is to select where to apply the domain-specific textural pattern. However, this is not mentioned explicitly. Consider adding are more explicit discussion of the mask's purpose.\n  * Sec. 3.1 introduces a channel-last notation ($w \\times h \\times c$) but the paragraph \"Structural mask-producing Layer\" assumes $c \\times h \\times w$, i.e. the reverse. Consolidate these formal disagreements.\n  * Also, I think it is helpful in the paragraph \"Structural mask-producing Layer\" to make the construction of coordinate attention explicit, instead of only referencing Hou et al. (2021). This will make the paper more self-sufficient and make the method easier to understand.\n  * Eq. 9 is detached from the corresponding explanation, which is earlier (above Eq. 8). Consider restructuring this paragraph to more tightly couple explanations and formalization.\n  * \"threshold-based uncertainty filtering\" is unclear to me. I understand that the method simply takes the predictions from the source domain classifier (i.e., the most likely label) as pseudo-labels for the target domain classifier. I do not understand the connection to uncertainty and filtering. Please clarify this point.\n  * The most important point is to give a high-level overview of the method and losses at the beginning of Sec. 3. This will make the description easier to follow, given the complexity and high number of components. The division into architecture and loss terms is already helpful, and having an overview in the beginning that mentions all components, their roles, and possible relations to each other will increase clarity further.\n\nFinally, there are at least two sentences that require rephrasing:\n  * \"A mask-producing layer $f_\\text{mask}$ is a function that takes as input an image and produces the mask, is a fully convolutional subnetwork of\": The double \"is a\" is confusing and likely not grammatically correct.\n  * \"and weak augmentations, which are the original image\": I understand from the context that, in this case, no augmentations are performed. However, equating this with weak augmentations is confusing.", "broader_impact_concerns": "No broader impact concerns. Broader Impact Statement not necessary.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)