mlsanityData to 14 Oct 2026

A Bit More Bayesian: Domain-Invariant Learning with Uncertainty

arXiv 2105.04030 ↗First posted 9 May 2021cs.LGcited by 27

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Abstract (arXiv)

Domain generalization is challenging due to the domain shift and the uncertainty caused by the inaccessibility of target domain data. In this paper, we address both challenges with a probabilistic framework based on variational Bayesian inference, by incorporating uncertainty into neural network weights. We couple domain invariance in a probabilistic formula with the variational Bayesian inference. This enables us to explore domain-invariant learning in a principled way. Specifically, we derive domain-invariant representations and classifiers, which are jointly established in a two-layer Bayesian neural network. We empirically demonstrate the effectiveness of our proposal on four widely used cross-domain visual recognition benchmarks. Ablation studies validate the synergistic benefits of our Bayesian treatment when jointly learning domain-invariant representations and classifiers for domain generalization. Further, our method consistently delivers state-of-the-art mean accuracy on all benchmarks.

Key facts. Cited by 27 papers in the map (data through 14 Oct 2026). Builds on 43 of its 54 references that are in the map. First posted on arXiv 9 May 2021.

Builds on

43 of 54 references.

References · 43

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31 more
  1. 2020Out-of-Distribution Generalization via Risk Extrapolation (REx)cited by 742
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  3. 2021Domain Generalization with MixStylecited by 566
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  6. 2015Variational Dropout and the Local Reparameterization Trickcited by 490
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  8. 2019Domain Generalization by Solving Jigsaw Puzzlescited by 450
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Cited by year

Citing papers per year: 2021: 2; 2022: 6; 2023: 8; 2024: 8; 2025: 3.

27 papers.

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  1. 2022Causality Inspired Representation Learning for Domain Generalizationcited by 138
  2. 2024Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignmentcited by 29
  3. 2022Learning to Generalize across Domains on Single Test Samplescited by 24
  4. 2024Any-Shift Prompting for Generalization over Distributionscited by 24
  5. 2023Adversarial Bayesian Augmentation for Single-Source Domain Generalizationcited by 22
  6. 2023Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptationcited by 20
  7. 2022Variational Model Perturbation for Source-Free Domain Adaptationcited by 18
  8. 2021Hierarchical Variational Memory for Few-shot Learning Across Domainscited by 14
  9. 2023Energy-Based Test Sample Adaptation for Domain Generalizationcited by 14
  10. 2022A General Framework for quantifying Aleatoric and Epistemic uncertainty in Graph Neural Networkscited by 12
  11. 2023A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigmcited by 10
  12. 2022Causal Inference via Style Transfer for Out-of-distribution Generalisationcited by 8
15 more
  1. 2023Moderately Distributional Exploration for Domain Generalizationcited by 8
  2. 2024Domain Generalization with Small Datacited by 6
  3. 2024QUOTA: Quantifying Objects with Text-to-Image Models for Any Domaincited by 3
  4. 2021Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminationscited by 2
  5. 2024TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correctioncited by 2
  6. 2025GeneralizeFormer: Layer-Adaptive Model Generation across Test-Time Distribution Shiftscited by 2
  7. 2023Calibration-based Dual Prototypical Contrastive Learning Approach for Domain Generalization Semantic Segmentationcited by 1
  8. 2023Bayesian Domain Invariant Learning via Posterior Generalization of Parameter Distributionscited by 1
  9. 2024R'enyi Neural Processescited by 1
  10. 2024Towards Robust Uncertainty-Aware Incomplete Multi-View Classificationcited by 1
  11. 2025VirDA: Reusing Backbone for Unsupervised Domain Adaptation with Visual Reprogrammingcited by 1
  12. 2022Association Graph Learning for Multi-Task Classification with Category Shiftscited by 0
  13. 2023Be Bayesian by Attachments to Catch More Uncertaintycited by 0
  14. 2024Causal Understanding For Video Question Answeringcited by 0
  15. 2025PEdger++: Practical Edge Detection via Assembling Cross Informationcited by 0