FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment

Amit Sethi; Sunny Gupta; Varunav Singh; Vinay Sutar

REVIEW 5 major objections 5 minor 39 references

FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment

T0 review · 5 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read FedAlign claims that augmenting each federated client's local batches with cross-client style statistics and aligning both representations and predictions yields top generalization to unseen domains under privacy constraints.

desk verdict The paper's advertised cross-client mechanism is missing from its own algorithm and loss, so the reported SOTA gains are unverifiable; the local MixStyle+alignment package is plausible and deserves referee attention to get fixed. read the letter →

arxiv 2501.15486 v1 pith:OYZ3WQ5N submitted 2025-01-26 cs.LG cs.AIcs.CVcs.DC

Sunny Gupta , Vinay Sutar , Varunav Singh , Amit Sethi This is my paper · ORCID

classification cs.LGcs.AIcs.CVcs.DC

keywords federatedlearningdomaingeneralizationdomain-invariantfeaturesMixStylesupervisedcontrastivelossJensen-Shannondivergencefeaturealignmentprivacy-preserving

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes FedAlign, a way to train a single image classifier across several clients that each hold data from a different visual domain, so that the shared model works on a domain none of the clients have seen. The central claim is that two additions to ordinary federated training—a cross-client feature extension that diversifies each client's style statistics, and a dual-stage alignment that pulls representations and predictions closer across augmented views—let federated models beat existing federated domain generalization baselines. On the four benchmarks tested, FedAlign reports the highest average accuracy, for instance 82.96% on PACS versus 79.85% for the strongest baseline CCST, while transmitting only model parameters and sample statistics rather than raw data.

What carries the argument

The central object is the per-client augmented-training loop: for a batch $X$, generate $X^{(1)} = \mathcal{M}(X)$ and $X^{(2)} = \mathcal{M}(X)$ via a MixStyle module $\mathcal{M}$ that interpolates channel-wise means and standard deviations, then compute representations and predictions for all three views and minimize the combined classification, representation-alignment, and prediction-alignment loss. The cross-client component is the sharing of channel-wise style statistics between clients and server, which the paper says broadens each client's effective domain coverage without exposing raw data.

What would settle it

Run FedAlign with the cross-client statistics sharing disabled, keeping only each client's local MixStyle and the dual-stage alignment losses; if the average PACS accuracy stays at 82.96% or the gap over CCST disappears, the cross-client extension is not the source of the gains.

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Extended reading notes

Core claim

FedAlign's central claim is that federated domain generalization improves when each client augments its local batches with MixStyle-style style interpolation and is trained with a total loss $L = L_{CLS} + \lambda_1(L_{SC} + L_{RC}) + \lambda_2 L_{JS}$, where $L_{SC}$ is a supervised contrastive loss on representations, $L_{RC}$ is an MSE consistency loss between original and augmented representations, and $L_{JS}$ is a Jensen–Shannon divergence between predictions on original and augmented views. The paper reports consistent superiority over the compared methods across PACS, OfficeHome, Caltech-10, and miniDomainNet, and presents t-SNE visualizations as evidence that the learned representations are more compact and class-separated than those of federated baselines.

Load-bearing premise

The load-bearing premise is that clients and the server actually exchange channel-wise style statistics, so that the feature-extension step genuinely spans cross-client styles rather than only local ones.

Editorial extensions

If this is right

On PACS, FedAlign reports an average accuracy of 82.96%, beating the best prior federated domain generalization method CCST at 79.85%.
The same pattern holds on OfficeHome (68.03%), Caltech-10 (93.72%), and miniDomainNet (63.76%), with FedAlign reaching the top accuracy in each target domain on PACS and miniDomainNet.
FedAlign's advantage persists as the number of participating clients changes, whereas the comparison methods' accuracy deteriorates more markedly with larger client populations.
Sharing only channel-wise statistics rather than raw data or high-dimensional feature maps keeps communication and computation overhead minimal, according to the paper.
The learned representations are qualitatively more compact and class-separated under t-SNE, which the paper interprets as evidence of domain-invariant features.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Inference: if FedAlign works as claimed, the dual-stage alignment losses may be the main driver, and a simplified baseline using only local MixStyle plus consistency losses could match the reported accuracy.
Inference: the privacy guarantee is weaker than stated, since channel-wise style statistics can still leak information about local data distributions; a reconstruction or attribute-inference attack could quantify the residual risk.
Inference: the method should transfer to non-image modalities whenever style statistics can be defined, such as sensor or text embeddings, where domain shifts follow similar patterns.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

You should know two things before spending time on this one. First, the load-bearing component in the abstract and intro—"cross-client feature extension"—does not appear in the algorithm the paper gives. Algorithm 1 communicates only model parameters; no channel-wise style statistics are collected, aggregated, or redistributed. The MixStyle module is deferred to "Algorithm 2," which does not exist. Second, Section 3.4 describes an adversarial domain discriminator, but its objective is absent from the total loss in Eq. (16), and no update rule is given. So the central mechanism that makes FedAlign "cross-client" and privacy-preserving is unspecified.

The paper is not without merit. The identity of the method as written is local MixStyle augmentation plus three alignment losses: supervised contrastive (LSC), MSE representation consistency (LRC), and Jensen-Shannon prediction consistency (LJS). That package is reasonable and known to help in DG. The experiments follow the standard leave-one-domain-out protocol and compare against the right baselines (FedAvg, FedProx, FedADG, FedSR, FedIIR, CCST, GA). The reported gains—e.g., 82.96% average on PACS vs. 79.85% for CCST—are plausible and, if reproducible, would be a small but useful improvement. The paper should get credit for combining these known modules in a federated setting and for testing on four benchmarks.

The soft spots, however, are load-bearing. The algorithm/equation mismatch means the "cross-client" claim is not independently checkable from the manuscript. A reader cannot tell whether the reported numbers come from the method advertised or from a weaker local-only variant. There are also no standard deviations, no hyperparameter values (lambda_1, lambda_2, alpha, number of clients, etc.), and no code release. The dataset description is sloppy—Caltech-101 is described with a reference to the Caltech-256 paper. These are fixable, but in the current form the paper's central claim is unverifiable.

Who is this for? Researchers in federated domain generalization who want to see whether MixStyle-style augmentation with alignment losses transfers to FL. It could be a useful baseline. I would not cite it as is.

For peer review: I'd send it out rather than desk-reject, because the experimental question is worth referee time and the missing details could be supplied. The authors should be pushed hard to provide the actual algorithm, code, hyperparameters, error bars, and a corrected narrative that matches the implemented method. If they can do that, the paper has a chance. In the current state, it is not a reliable report of the stated method. Recommendation: send to review with major-revision expectations.

Referee Report

5 major / 5 minor

Summary. The paper proposes FedAlign, a federated domain generalization method that combines a cross-client feature extension module (based on MixStyle-style style statistics interpolation and sharing) with a dual-stage alignment module (supervised contrastive loss, representation consistency loss, and Jensen-Shannon divergence on predictions). The authors claim state-of-the-art accuracy on PACS, OfficeHome, miniDomainNet, and Caltech-101 benchmarks under a federated leave-one-domain-out protocol, with minimal communication and privacy overhead. The central empirical claim is that this combination of modules yields superior generalization to unseen domains while preserving privacy.

Significance. If fully validated, FedAlign would be a genuinely useful contribution to federated domain generalization, offering a light-weight alternative to adversarial or generative approaches. The paper targets a real problem—limited domain diversity on clients under privacy constraints—and the proposed loss combination is plausible as a mechanism for learning domain-invariant features. However, the manuscript as written does not substantiate the central claim because the cross-client feature-sharing machinery described in the text and figure is absent from the executable algorithm, and the adversarial component is never integrated into the optimization. The strengths are limited to a plausible idea and an apparently competitive table of numbers; reproducibility is currently impossible given the missing implementation details and hyperparameter values. The significance is therefore conditional on a substantial revision that reconciles the description with the actual executed method.

major comments (5)

[Algorithm 1 and Fig. 2] The central described mechanism, cross-client feature extension via sharing of sample statistics (Eqs. 7–10), is not implemented in Algorithm 1. The server loop (lines 3–10) only receives updated model parameters and broadcasts global parameters; no channel-wise mean/variance statistics are collected, aggregated, or redistributed, contrary to Fig. 2's caption that 'clients share local model parameters and sample statistics with the server.' Consequently, the executed method appears to be local MixStyle augmentation plus alignment losses, not the cross-client feature transfer claimed in the abstract. The reported accuracy gains cannot be attributed to the paper's distinctive contribution.
[Sec. 3.4, Eq. (16)] Section 3.4 introduces an adversarial domain discriminator, but no adversarial loss appears in the total loss (Eq. 16), no update rule for the discriminator is given, and Algorithm 1 contains no discriminator training step. The paper therefore describes a component that has no identifiable effect on the optimized objective or the reported results. Either the adversarial module is unused, in which case it should be removed, or it is used and the manuscript fails to specify how—either way, the current text is inconsistent.
[Section 3.3 and Algorithm 2] The MixStyle module M() is said to be described in 'Algorithm 2', but no Algorithm 2 appears in the manuscript. The claimed 'clustering' and 'probabilistic sampling weights' enhancements are described only verbally; there are no equations, no definitions of the clustering procedure, and no statement of how the cluster weights enter the sampling of λ in Eqs. (9)–(10). These are not cosmetic omissions: they are necessary to reproduce the method and to assess whether the claimed cross-client extension is actually present.
[Section 4 (Experiments) and Table 1] The experimental section omits standard deviations, the number of clients, the exact non-IID partitioning procedure, and the values of hyperparameters λ1, λ2, α, batch size, mix_feat size, and clustering parameters. With only three runs reported and no variance, the claim in Section 5.1 that FedAlign 'consistently outperforms all baseline methods across the evaluated datasets' cannot be statistically assessed. The reported margins over CCST (e.g., 82.96 vs 79.85 on PACS) may be within noise, and without these details the result is not independently checkable.
[Eqs. (12)–(13), notation] The supervised contrastive loss in Eq. (12) is written only for a single augmented view convention, while Algorithm 1 computes L_SC(Z^(1), Z) + L_SC(Z^(2), Z), but the definition in Eq. (12) does not specify how multiple views are handled (e.g., whether views are included in the positive set). The representation consistency loss in Eq. (13) uses |mix_feat| without defining it, and the notation h(X_aug) is ambiguous because Algorithm 1 calls the augmented batches X^(1) and X^(2). These ambiguities prevent a reader from implementing the loss exactly as intended.

minor comments (5)

[Abstract and Section 1] The phrase 'superior generalization to unseen domains' is a strong claim that is only supported by a single table without variance; please phrase as 'competitive' until statistical significance is established.
[Section 2.2] The reference to 'CycleGAN' for style transfer is tangential to the federated setting; consider citing a more relevant federated style transfer baseline or removing it.
[Table 1 and Section 4] The dataset name 'Caltech-10' in Table 1 is inconsistent with 'Caltech-101' in the text; also, the column header abbreviates domains inconsistently (e.g., 'C', 'P', 'R', 'S' without a legend).
[Section 3.4] The sentence beginning 'This adversarial mechanism effectively mitigates domain shift' is asserted without any supporting ablation; consider removing or substantiating.
[Section 4] The paper says 'We follow the partitioning strategy presented in Section 3.1', but Section 3.1 only defines the problem setup and does not describe a concrete data-partitioning procedure. Please provide the explicit split (e.g., number of clients, Dirichlet concentration, or per-client domain composition).

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: FedAlign's contribution is empirical and benchmarked externally; the under-specified cross-client mechanism is a completeness gap, not a circular derivation.

full rationale

The paper's central claim is an empirical performance comparison on standard external benchmarks (PACS, OfficeHome, Caltech-101, miniDomainNet), and the reported accuracies are not derived from the method's own inputs by construction. The total loss in Eq. (16) is explicitly defined in terms of cross-entropy, supervised contrastive, representation consistency, and Jensen-Shannon divergence losses, all of which are computed from model outputs and locally augmented samples; no fitted parameter is renamed as a prediction, and no theoretical result is reduced to its own assumptions. The paper contains no load-bearing self-citations, no imported uniqueness theorem, and no ansatz smuggled in via citation; MixStyle is cited as an external, existing augmentation method. The skeptical observations about the missing cross-client statistics exchange in Algorithm 1 and the absence of the Sec. 3.4 adversarial term in Eq. (16) identify under-specification and a potential gap between the claimed mechanism and the executable description, but they do not constitute circularity under the defined criteria: the method is not self-referential, and the empirical result is not forced by definition. Therefore, while reproducibility and completeness concerns are legitimate, the circularity score is 0.

Assumptions & free parameters 7 free parameters · 4 assumptions · 2 invented entities

The central method depends on several unstated hyperparameters, an under-specified style augmentation scheme, and two introduced components (the cross-client extension module and the adversarial discriminator) that are not present in the algorithm. The privacy claim rests on an unproved security assumption. These count against the paper's independent grounding.

free parameters (7)

lambda_1 = not reported
Weight on representation alignment loss (L_SC + L_RC) in Eq. 16; not specified in Section 4, so results cannot be reproduced.
lambda_2 = not reported
Weight on JS divergence prediction alignment loss in Eq. 16; not reported.
alpha = not reported
Beta distribution parameter for MixStyle interpolation (Eq. 9-10); no value given.
batch_size = not reported
Supervised contrastive loss (Eq. 12) is sensitive to batch size and class balance; no batch size given.
number_of_clients = not reported
Data partitioning says 'predefined number of clients' but no value; Figure 4 varies it without specifying defaults for Table 1.
mix_feat = not reported
Which feature layers receive MixStyle augmentation is not specified; Eq. 13 uses |mix_feat|.
clustering_num_clusters = not reported
Section 3.3 introduces clustering but no algorithm or hyperparameters; impossible to reproduce.

assumptions (4)

domain assumption Channel-wise feature statistics (mean and standard deviation) are sufficient descriptors of style for simulating domain shifts.
MixStyle equations (7)-(11) rely on this; no empirical validation in this paper.
domain assumption Sharing channel-wise statistics with the server preserves privacy.
Section 4 asserts adversaries cannot reconstruct samples from these statistics, without proof or reference to known reconstruction attacks.
domain assumption Each client's local data corresponds to a unique domain.
Figure 1 states this, but Section 3.1 writes D_k subset S_i (each domain spread across clients); the two setups are inconsistent.
ad hoc to paper The clustering and probabilistic sampling enhancements in Section 3.3 are well-defined and implemented.
No equations, algorithm, or implementation details are provided for these enhancements; the paper assumes they contribute to the reported gains.

invented entities (2)

Cross-client feature extension module
purpose: Perturbs and transfers domain-invariant features across clients to broaden local domain diversity.
Described in Sections 1 and 3.2 and Figure 2, but absent from Algorithm 1; no independent validation or ablation.
Domain discriminator for adversarial training
purpose: Distinguishes original from augmented representations to enforce domain invariance.
Section 3.4 introduces it, but it does not appear in Eq. 16 or Algorithm 1; no discriminator loss or update rule is given.

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Cite this review

Pith. "Pith review of FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment." pith.science (2026). https://pith.science/paper/OYZ3WQ5N

@misc{pith2026250115486,
  author       = {Pith},
  title        = {Pith review of: FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OYZ3WQ5N}},
  note         = {Machine review of arXiv:2501.15486}
}

read the original abstract

Federated Learning (FL) offers a decentralized paradigm for collaborative model training without direct data sharing, yet it poses unique challenges for Domain Generalization (DG), including strict privacy constraints, non-i.i.d. local data, and limited domain diversity. We introduce FedAlign, a lightweight, privacy-preserving framework designed to enhance DG in federated settings by simultaneously increasing feature diversity and promoting domain invariance. First, a cross-client feature extension module broadens local domain representations through domain-invariant feature perturbation and selective cross-client feature transfer, allowing each client to safely access a richer domain space. Second, a dual-stage alignment module refines global feature learning by aligning both feature embeddings and predictions across clients, thereby distilling robust, domain-invariant features. By integrating these modules, our method achieves superior generalization to unseen domains while maintaining data privacy and operating with minimal computational and communication overhead.

Figures

Figures reproduced from arXiv: 2501.15486 by the authors.

**Figure 2.** Overview of FedAlign: Clients share local model parameters and sample statistics with the server, which aggregates and redistributes [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** t-SNE visualization of the representation distribution using FedSR. The representations show domain-specific clusters with notice [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Average test accuracy (%) versus the number of participating clients. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

Discussion (0). Continue with ORCID to comment.

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Works this paper leans on

39 extracted references · 29 canonical work pages

[1]

Wasserstein generative adversarial networks

Martin Arjovsky, Soumith Chintala, and L \'e on Bottou. Wasserstein generative adversarial networks. In International conference on machine learning , pages 214--223. PMLR, 2017

2017
[2]

Federated domain generalization for image recognition via cross-client style transfer

Junming Chen, Meirui Jiang, Qi Dou, and Qifeng Chen. Federated domain generalization for image recognition via cross-client style transfer. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages 361--370, 2023

work page 2023
[3]

Unsupervised domain adaptation by backpropagation

Yaroslav Ganin and Victor Lempitsky. Unsupervised domain adaptation by backpropagation. In International conference on machine learning , pages 1180--1189. PMLR, 2015

work page 2015
[4]

Domain-adversarial training of neural networks

Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Fran c ois Laviolette, Mario March, and Victor Lempitsky. Domain-adversarial training of neural networks. Journal of machine learning research , 17(59):1--35, 2016

work page 2016
[5]

Dlow: Domain flow for adaptation and generalization

Rui Gong, Wen Li, Yuhua Chen, and Luc Van Gool. Dlow: Domain flow for adaptation and generalization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 2477--2486, 2019

work page 2019
[6]

Caltech-256 object category dataset

Gregory Griffin, Alex Holub, and Pietro Perona. Caltech-256 object category dataset. Technical report, California Institute of Technology, 2007

work page 2007
[7]

Out-of-distribution generalization of federated learning via implicit invariant relationships

Yaming Guo, Kai Guo, Xiaofeng Cao, Tieru Wu, and Yi Chang. Out-of-distribution generalization of federated learning via implicit invariant relationships. In International Conference on Machine Learning , pages 11905--11933. PMLR, 2023

work page 2023
[8]

Arbitrary style transfer in real-time with adaptive instance normalization

Xun Huang and Serge Belongie. Arbitrary style transfer in real-time with adaptive instance normalization. In Proceedings of the IEEE international conference on computer vision , pages 1501--1510, 2017

work page 2017

Show all 39 references

[9]

Deeper, broader and artier domain generalization

Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales. Deeper, broader and artier domain generalization. In Proceedings of the IEEE international conference on computer vision , pages 5542--5550, 2017

2017
[10]

A survey on federated learning systems: Vision, hype and reality for data privacy and protection

Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, and Bingsheng He. A survey on federated learning systems: Vision, hype and reality for data privacy and protection. IEEE Transactions on Knowledge and Data Engineering , 35(4):3347--3366, 2021

2021
[11]

Communication-efficient learning of deep networks from decentralized data

Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics , pages 1273--1282. PMLR, 2017

2017
[12]

Zero-shot knowledge transfer via adversarial belief matching

Paul Micaelli and Amos J Storkey. Zero-shot knowledge transfer via adversarial belief matching. Advances in Neural Information Processing Systems , 32, 2019

2019
[13]

A survey on security and privacy of federated learning

Viraaji Mothukuri, Reza M Parizi, Seyedamin Pouriyeh, Yan Huang, Ali Dehghantanha, and Gautam Srivastava. A survey on security and privacy of federated learning. Future Generation Computer Systems , 115:619--640, 2021

2021
[14]

Fedsr: A simple and effective domain generalization method for federated learning

A Tuan Nguyen, Philip Torr, and Ser Nam Lim. Fedsr: A simple and effective domain generalization method for federated learning. Advances in Neural Information Processing Systems , 35:38831--38843, 2022

2022
[15]

Domain generalization with interpolation robustness

Ragja Palakkadavath, Thanh Nguyen-Tang, Hung Le, Svetha Venkatesh, and Sunil Gupta. Domain generalization with interpolation robustness. In Asian Conference on Machine Learning , pages 1039--1054. PMLR, 2024

2024
[16]

Stablefdg: style and attention based learning for federated domain generalization

Jungwuk Park, Dong-Jun Han, Jinho Kim, Shiqiang Wang, Christopher Brinton, and Jaekyun Moon. Stablefdg: style and attention based learning for federated domain generalization. Advances in Neural Information Processing Systems , 36, 2024

2024
[17]

Federated adversarial domain adaptation

Xingchao Peng, Zijun Huang, Yizhe Zhu, and Kate Saenko. Federated adversarial domain adaptation. arXiv preprint arXiv:1911.02054 , 2019

1911 arXiv
[18]

Do imagenet classifiers generalize to imagenet? In International conference on machine learning , pages 5389--5400

Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar. Do imagenet classifiers generalize to imagenet? In International conference on machine learning , pages 5389--5400. PMLR, 2019

2019
[19]

Model-based domain generalization

Alexander Robey, George J Pappas, and Hamed Hassani. Model-based domain generalization. Advances in Neural Information Processing Systems , 34:20210--20229, 2021

2021
[20]

Very deep convolutional networks for large-scale image recognition

Karen Simonyan. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 , 2014

2014 arXiv
[21]

Deep coral: Correlation alignment for deep domain adaptation

Baochen Sun and Kate Saenko. Deep coral: Correlation alignment for deep domain adaptation. In Computer Vision--ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III 14 , pages 443--450. Springer, 2016

2016
[22]

Deep domain confusion: Maximizing for domain invariance

Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell. Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474 , 2014

2014 arXiv
[23]

Deep hashing network for unsupervised domain adaptation

Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan. Deep hashing network for unsupervised domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 5018--5027, 2017

2017
[24]

Addressing model vulnerability to distributional shifts over image transformation sets

Riccardo Volpi and Vittorio Murino. Addressing model vulnerability to distributional shifts over image transformation sets. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 7980--7989, 2019

2019
[25]

Generalizing to unseen domains via adversarial data augmentation

Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C Duchi, Vittorio Murino, and Silvio Savarese. Generalizing to unseen domains via adversarial data augmentation. Advances in neural information processing systems , 31, 2018

2018
[26]

Visual domain adaptation with manifold embedded distribution alignment

Jindong Wang, Wenjie Feng, Yiqiang Chen, Han Yu, Meiyu Huang, and Philip S Yu. Visual domain adaptation with manifold embedded distribution alignment. In Proceedings of the 26th ACM international conference on Multimedia , pages 402--410, 2018

2018
[27]

Transfer learning with dynamic distribution adaptation

Jindong Wang, Yiqiang Chen, Wenjie Feng, Han Yu, Meiyu Huang, and Qiang Yang. Transfer learning with dynamic distribution adaptation. ACM Transactions on Intelligent Systems and Technology (TIST) , 11(1):1--25, 2020

2020
[28]

Robust and generalizable visual representation learning via random convolutions

Zhenlin Xu, Deyi Liu, Junlin Yang, Colin Raffel, and Marc Niethammer. Robust and generalizable visual representation learning via random convolutions. arXiv preprint arXiv:2007.13003 , 2020

2007 arXiv
[29]

Federated adversarial domain hallucination for privacy-preserving domain generalization

Qinwei Xu, Ruipeng Zhang, Ya Zhang, Yi-Yan Wu, and Yanfeng Wang. Federated adversarial domain hallucination for privacy-preserving domain generalization. IEEE Transactions on Multimedia , 26:1--14, 2023

2023
[30]

Fda: Fourier domain adaptation for semantic segmentation

Yanchao Yang and Stefano Soatto. Fda: Fourier domain adaptation for semantic segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 4085--4095, 2020

2020
[31]

Federated multi-target domain adaptation

Chun-Han Yao, Boqing Gong, Hang Qi, Yin Cui, Yukun Zhu, and Ming-Hsuan Yang. Federated multi-target domain adaptation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages 1424--1433, 2022

2022
[32]

Fedmix: Approximation of mixup under mean augmented federated learning

Tehrim Yoon, Sumin Shin, Sung Ju Hwang, and Eunho Yang. Fedmix: Approximation of mixup under mean augmented federated learning. arXiv preprint arXiv:2107.00233 , 2021

2021 arXiv
[33]

mixup: Beyond empirical risk minimization

Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. In International Conference on Learning Representations , 2018

2018
[34]

Federated learning with domain generalization

Liling Zhang, Xinyu Lei, Yichun Shi, Hongyu Huang, and Chao Chen. Federated learning with domain generalization. arXiv preprint arXiv:2111.10487 , 2021

2021 arXiv
[35]

Federated domain generalization with generalization adjustment

Ruipeng Zhang, Qinwei Xu, Jiangchao Yao, Ya Zhang, Qi Tian, and Yanfeng Wang. Federated domain generalization with generalization adjustment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 3954--3963, 2023

2023
[36]

Domain adaptive ensemble learning

Kaiyang Zhou, Yongxin Yang, Yu Qiao, and Tao Xiang. Domain adaptive ensemble learning. IEEE Transactions on Image Processing , 30:8008--8018, 2021

2021
[37]

Domain generalization: A survey

Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy. Domain generalization: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence , 45(4):4396--4415, 2022

2022
[38]

Unpaired image-to-image translation using cycle-consistent adversarial networks

Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision , pages 2223--2232, 2017

2017
[39]

write newline

" write newline "" before.all 'output.state := FUNCTION fin.entry add.period write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence '...

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [1]

Wasserstein generative adversarial networks

Martin Arjovsky, Soumith Chintala, and L \'e on Bottou. Wasserstein generative adversarial networks. In International conference on machine learning , pages 214--223. PMLR, 2017

2017

[2] [2]

Federated domain generalization for image recognition via cross-client style transfer

Junming Chen, Meirui Jiang, Qi Dou, and Qifeng Chen. Federated domain generalization for image recognition via cross-client style transfer. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages 361--370, 2023

work page 2023

[3] [3]

Unsupervised domain adaptation by backpropagation

Yaroslav Ganin and Victor Lempitsky. Unsupervised domain adaptation by backpropagation. In International conference on machine learning , pages 1180--1189. PMLR, 2015

work page 2015

[4] [4]

Domain-adversarial training of neural networks

Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Fran c ois Laviolette, Mario March, and Victor Lempitsky. Domain-adversarial training of neural networks. Journal of machine learning research , 17(59):1--35, 2016

work page 2016

[5] [5]

Dlow: Domain flow for adaptation and generalization

Rui Gong, Wen Li, Yuhua Chen, and Luc Van Gool. Dlow: Domain flow for adaptation and generalization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 2477--2486, 2019

work page 2019

[6] [6]

Caltech-256 object category dataset

Gregory Griffin, Alex Holub, and Pietro Perona. Caltech-256 object category dataset. Technical report, California Institute of Technology, 2007

work page 2007

[7] [7]

Out-of-distribution generalization of federated learning via implicit invariant relationships

Yaming Guo, Kai Guo, Xiaofeng Cao, Tieru Wu, and Yi Chang. Out-of-distribution generalization of federated learning via implicit invariant relationships. In International Conference on Machine Learning , pages 11905--11933. PMLR, 2023

work page 2023

[8] [8]

Arbitrary style transfer in real-time with adaptive instance normalization

Xun Huang and Serge Belongie. Arbitrary style transfer in real-time with adaptive instance normalization. In Proceedings of the IEEE international conference on computer vision , pages 1501--1510, 2017

work page 2017