# Feature-based Style Randomization for Domain Generalization

arXiv: https://arxiv.org/abs/2106.03171 · HTML: https://mlsanity.com/paper/2106.03171/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 55 of its 90 references that are in the map.
- First posted on arXiv 6 Jun 2021.

## Builds on (most cited first, 30 of 55)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Deep Hashing Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1706.07522/) (2017)
- [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017)
- [Deeper, Broader and Artier Domain Generalization](https://mlsanity.com/paper/1710.03077/) (2017)
- [Universal adversarial perturbations](https://mlsanity.com/paper/1610.08401/) (2016)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [Maximum Classifier Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1712.02560/) (2017)
- [Domain Generalization via Invariant Feature Representation](https://mlsanity.com/paper/1301.2115/) (2013)
- [Domain Generalization with MixStyle](https://mlsanity.com/paper/2104.02008/) (2021)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [Domain Generalization by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1903.06864/) (2019)
- [Confidence Regularized Self-Training](https://mlsanity.com/paper/1908.09822/) (2019)
- [Domain Generalization via Model-Agnostic Learning of Semantic Features](https://mlsanity.com/paper/1910.13580/) (2019)
- [Unsupervised Domain Adaptation with Residual Transfer Networks](https://mlsanity.com/paper/1602.04433/) (2016)
- [Bidirectional Learning for Domain Adaptation of Semantic Segmentation](https://mlsanity.com/paper/1904.10620/) (2019)
- [Learning to Generate Novel Domains for Domain Generalization](https://mlsanity.com/paper/2007.03304/) (2020)
- [Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation](https://mlsanity.com/paper/1809.09478/) (2018)
- [Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization](https://mlsanity.com/paper/1804.06516/) (2018)
- [A Fourier-based Framework for Domain Generalization](https://mlsanity.com/paper/2105.11120/) (2021)
- [Generalizing Across Domains via Cross-Gradient Training](https://mlsanity.com/paper/1804.10745/) (2018)
- [Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data](https://mlsanity.com/paper/1909.00889/) (2019)

## Built on by (most cited first, 30 of 34)

- [Understanding and Mitigating Overfitting in Prompt Tuning for Vision-Language Models](https://mlsanity.com/paper/2211.02219/) (2022)
- [ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalization](https://mlsanity.com/paper/2303.11674/) (2023)
- [DomainDrop: Suppressing Domain-Sensitive Channels for Domain Generalization](https://mlsanity.com/paper/2308.10285/) (2023)
- [MVDG: A Unified Multi-view Framework for Domain Generalization](https://mlsanity.com/paper/2112.12329/) (2021)
- [DomainAdaptor: A Novel Approach to Test-time Adaptation](https://mlsanity.com/paper/2308.10297/) (2023)
- [Rethinking Domain Generalization: Discriminability and Generalizability](https://mlsanity.com/paper/2309.16483/) (2023)
- [Learn to Preserve and Diversify: Parameter-Efficient Group with Orthogonal Regularization for Domain Generalization](https://mlsanity.com/paper/2407.15085/) (2024)
- [Adaptive Domain Generalization via Online Disagreement Minimization](https://mlsanity.com/paper/2208.01996/) (2022)
- [Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts](https://mlsanity.com/paper/2211.04393/) (2022)
- [NormAUG: Normalization-guided Augmentation for Domain Generalization](https://mlsanity.com/paper/2307.13492/) (2023)
- [MultiMatch: Multi-task Learning for Semi-supervised Domain Generalization](https://mlsanity.com/paper/2208.05853/) (2022)
- [Cross-Domain Feature Augmentation for Domain Generalization](https://mlsanity.com/paper/2405.08586/) (2024)
- [Exclusive Style Removal for Cross Domain Novel Class Discovery](https://mlsanity.com/paper/2406.18140/) (2024)
- [Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models](https://mlsanity.com/paper/2411.16018/) (2024)
- [LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration](https://mlsanity.com/paper/2308.14596/) (2023)
- [START: A Generalized State Space Model with Saliency-Driven Token-Aware Transformation](https://mlsanity.com/paper/2410.16020/) (2024)
- [Tackling Data Bias in Painting Classification with Style Transfer](https://mlsanity.com/paper/2301.02524/) (2023)
- [A Novel Cross-Perturbation for Single Domain Generalization](https://mlsanity.com/paper/2308.00918/) (2023)
- [PLACE dropout: A Progressive Layer-wise and Channel-wise Dropout for Domain Generalization](https://mlsanity.com/paper/2112.03676/) (2021)
- [Style-Agnostic Reinforcement Learning](https://mlsanity.com/paper/2208.14863/) (2022)
- [Learning to Generalize towards Unseen Domains via a Content-Aware Style Invariant Model for Disease Detection from Chest X-rays](https://mlsanity.com/paper/2302.13991/) (2023)
- [Generalizable Metric Network for Cross-domain Person Re-identification](https://mlsanity.com/paper/2306.11991/) (2023)
- [Constrained Maximum Cross-Domain Likelihood for Domain Generalization](https://mlsanity.com/paper/2210.04155/) (2022)
- [BiasBed -- Rigorous Texture Bias Evaluation](https://mlsanity.com/paper/2211.13190/) (2022)
- [Calibration-based Dual Prototypical Contrastive Learning Approach for Domain Generalization Semantic Segmentation](https://mlsanity.com/paper/2309.14282/) (2023)
- [Explainable AI the Latest Advancements and New Trends](https://mlsanity.com/paper/2505.07005/) (2025)
- [Prototypical Progressive Alignment and Reweighting for Generalizable Semantic Segmentation](https://mlsanity.com/paper/2507.11955/) (2025)
- [Self-Training with Dynamic Weighting for Robust Gradual Domain Adaptation](https://mlsanity.com/paper/2510.13864/) (2025)
- [Prompt-Driven Simulation with Feature Perturbation for Cross-Domain Few-Shot Object Detection](https://mlsanity.com/paper/2608.01348/) (2026)
- [Patch-aware Batch Normalization for Improving Cross-domain Robustness](https://mlsanity.com/paper/2304.02848/) (2023)

## Abstract (arXiv)

As a recent noticeable topic, domain generalization (DG) aims to first learn a generic model on multiple source domains and then directly generalize to an arbitrary unseen target domain without any additional adaption. In previous DG models, by generating virtual data to supplement observed source domains, the data augmentation based methods have shown its effectiveness. To simulate the possible unseen domains, most of them enrich the diversity of original data via image-level style transformation. However, we argue that the potential styles are hard to be exhaustively illustrated and fully augmented due to the limited referred styles, leading the diversity could not be always guaranteed. Unlike image-level augmentation, we in this paper develop a simple yet effective feature-based style randomization module to achieve feature-level augmentation, which can produce random styles via integrating random noise into the original style. Compared with existing image-level augmentation, our feature-level augmentation favors a more goal-oriented and sample-diverse way. Furthermore, to sufficiently explore the efficacy of the proposed module, we design a novel progressive training strategy to enable all parameters of the network to be fully trained. Extensive experiments on three standard benchmark datasets, i.e., PACS, VLCS and Office-Home, highlight the superiority of our method compared to the state-of-the-art methods.