# Unknown Domain Inconsistency Minimization for Domain Generalization arXiv: https://arxiv.org/abs/2403.07329 ยท HTML: https://mlsanity.com/paper/2403.07329/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 45 of its 58 references that are in the map. - First posted on arXiv 12 Mar 2024. ## Builds on (most cited first, 30 of 45) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014) - [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017) - [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017) - [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015) - [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019) - [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019) - [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019) - [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020) - [Moment Matching for Multi-Source Domain Adaptation](https://mlsanity.com/paper/1812.01754/) (2018) - [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016) - [Deep Hashing Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1706.07522/) (2017) - [Deeper, Broader and Artier Domain Generalization](https://mlsanity.com/paper/1710.03077/) (2017) - [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021) - [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021) - [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017) - [Out-of-Distribution Generalization via Risk Extrapolation (REx)](https://mlsanity.com/paper/2003.00688/) (2020) - [Domain Generalization with MixStyle](https://mlsanity.com/paper/2104.02008/) (2021) - [Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data](https://mlsanity.com/paper/1703.11008/) (2017) - [Self-Challenging Improves Cross-Domain Generalization](https://mlsanity.com/paper/2007.02454/) (2020) - [Learning to Learn Single Domain Generalization](https://mlsanity.com/paper/2003.13216/) (2020) - [ASAM: Adaptive Sharpness-Aware Minimization for Scale-Invariant Learning of Deep Neural Networks](https://mlsanity.com/paper/2102.11600/) (2021) - [Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization](https://mlsanity.com/paper/2109.02934/) (2021) - [Improve Unsupervised Domain Adaptation with Mixup Training](https://mlsanity.com/paper/2001.00677/) (2020) - [Adaptive Risk Minimization: Learning to Adapt to Domain Shift](https://mlsanity.com/paper/2007.02931/) (2020) - [Domain Generalization by Marginal Transfer Learning](https://mlsanity.com/paper/1711.07910/) (2017) - [Learning to Diversify for Single Domain Generalization](https://mlsanity.com/paper/2108.11726/) (2021) - [On Calibration and Out-of-domain Generalization](https://mlsanity.com/paper/2102.10395/) (2021) - [Learning explanations that are hard to vary](https://mlsanity.com/paper/2009.00329/) (2020) - [Domain Adaptation for Visual Applications: A Comprehensive Survey](https://mlsanity.com/paper/1702.05374/) (2017) ## Built on by (most cited first, 7 of 7) - [Prompt Disentanglement via Language Guidance and Representation Alignment for Domain Generalization](https://mlsanity.com/paper/2507.02288/) (2025) - [Reasoning-Driven Multimodal LLM for Domain Generalization](https://mlsanity.com/paper/2602.23777/) (2026) - [DUET: Optimizing Training Data Mixtures via Feedback from Unseen Evaluation Tasks](https://mlsanity.com/paper/2502.00270/) (2025) - [Adversarial Domain Prompt Tuning and Generation for Single Domain Generalization](https://mlsanity.com/paper/2606.21736/) (2026) - [Reliable OOD Virtual Screening with Extrapolatory Pseudo-Label Matching](https://mlsanity.com/paper/2406.01825/) (2024) - [DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization](https://mlsanity.com/paper/2503.23430/) (2025) - [PAPT++: Risk-Aware Adversarial Tuning and Generation for Single Domain Generalization](https://mlsanity.com/paper/2609.04837/) (2026) ## Abstract (arXiv) The objective of domain generalization (DG) is to enhance the transferability of the model learned from a source domain to unobserved domains. To prevent overfitting to a specific domain, Sharpness-Aware Minimization (SAM) reduces source domain's loss sharpness. Although SAM variants have delivered significant improvements in DG, we highlight that there's still potential for improvement in generalizing to unknown domains through the exploration on data space. This paper introduces an objective rooted in both parameter and data perturbed regions for domain generalization, coined Unknown Domain Inconsistency Minimization (UDIM). UDIM reduces the loss landscape inconsistency between source domain and unknown domains. As unknown domains are inaccessible, these domains are empirically crafted by perturbing instances from the source domain dataset. In particular, by aligning the loss landscape acquired in the source domain to the loss landscape of perturbed domains, we expect to achieve generalization grounded on these flat minima for the unknown domains. Theoretically, we validate that merging SAM optimization with the UDIM objective establishes an upper bound for the true objective of the DG task. In an empirical aspect, UDIM consistently outperforms SAM variants across multiple DG benchmark datasets. Notably, UDIM shows statistically significant improvements in scenarios with more restrictive domain information, underscoring UDIM's generalization capability in unseen domains. Our code is available at urlhttps://github.com/SJShin-AI/UDIM.