# Rethinking Domain Generalization Baselines

arXiv: https://arxiv.org/abs/2101.09060 · HTML: https://mlsanity.com/paper/2101.09060/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 31 references that are in the map.
- First posted on arXiv 22 Jan 2021.

## Builds on (most cited first, 22 of 22)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Deep Hashing Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1706.07522/) (2017)
- [In Search of Lost Domain Generalization](https://mlsanity.com/paper/2007.01434/) (2020)
- [Learning Robust Global Representations by Penalizing Local Predictive Power](https://mlsanity.com/paper/1905.13549/) (2019)
- [Deeper, Broader and Artier Domain Generalization](https://mlsanity.com/paper/1710.03077/) (2017)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [Domain Generalization via Invariant Feature Representation](https://mlsanity.com/paper/1301.2115/) (2013)
- [Generalizing to Unseen Domains via Adversarial Data Augmentation](https://mlsanity.com/paper/1805.12018/) (2018)
- [Domain Generalization by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1903.06864/) (2019)
- [Self-Challenging Improves Cross-Domain Generalization](https://mlsanity.com/paper/2007.02454/) (2020)
- [Unified Deep Supervised Domain Adaptation and Generalization](https://mlsanity.com/paper/1709.10190/) (2017)
- [Domain Separation Networks](https://mlsanity.com/paper/1608.06019/) (2016)
- [Adversarial Domain Adaptation with Domain Mixup](https://mlsanity.com/paper/1912.01805/) (2019)
- [Episodic Training for Domain Generalization](https://mlsanity.com/paper/1902.00113/) (2019)
- [Deep Domain-Adversarial Image Generation for Domain Generalisation](https://mlsanity.com/paper/2003.06054/) (2020)
- [Domain Generalization Using a Mixture of Multiple Latent Domains](https://mlsanity.com/paper/1911.07661/) (2019)
- [Domain Generalization with Domain-Specific Aggregation Modules](https://mlsanity.com/paper/1809.10966/) (2018)
- [Self-supervised Domain Adaptation for Computer Vision Tasks](https://mlsanity.com/paper/1907.10915/) (2019)
- [One-Shot Unsupervised Cross-Domain Detection](https://mlsanity.com/paper/2005.11610/) (2020)

## Built on by (most cited first, 26 of 26)

- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [Prompt Vision Transformer for Domain Generalization](https://mlsanity.com/paper/2208.08914/) (2022)
- [Domain Prompt Learning for Efficiently Adapting CLIP to Unseen Domains](https://mlsanity.com/paper/2111.12853/) (2021)
- [Domain Generalization by Learning and Removing Domain-specific Features](https://mlsanity.com/paper/2212.07101/) (2022)
- [Variational Disentanglement for Domain Generalization](https://mlsanity.com/paper/2109.05826/) (2021)
- [Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges](https://mlsanity.com/paper/2309.07438/) (2023)
- [Towards Data-Free Domain Generalization](https://mlsanity.com/paper/2110.04545/) (2021)
- [A diverse large-scale building dataset and a novel plug-and-play domain generalization method for building extraction](https://mlsanity.com/paper/2208.10004/) (2022)
- [What Is Missing in IRM Training and Evaluation? Challenges and Solutions](https://mlsanity.com/paper/2303.02343/) (2023)
- [Gradient-Guided Annealing for Domain Generalization](https://mlsanity.com/paper/2502.20162/) (2025)
- [Closer Look at the Uncertainty Estimation in Semantic Segmentation under Distributional Shift](https://mlsanity.com/paper/2106.00076/) (2021)
- [Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts](https://mlsanity.com/paper/2211.04393/) (2022)
- [Moderately Distributional Exploration for Domain Generalization](https://mlsanity.com/paper/2304.13976/) (2023)
- [FDS: Feedback-guided Domain Synthesis with Multi-Source Conditional Diffusion Models for Domain Generalization](https://mlsanity.com/paper/2407.03588/) (2024)
- [Reappraising Domain Generalization in Neural Networks](https://mlsanity.com/paper/2110.07981/) (2021)
- [Conditional entropy minimization principle for learning domain invariant representation features](https://mlsanity.com/paper/2201.10460/) (2022)
- [PLACE dropout: A Progressive Layer-wise and Channel-wise Dropout for Domain Generalization](https://mlsanity.com/paper/2112.03676/) (2021)
- [Barycentric-alignment and reconstruction loss minimization for domain generalization](https://mlsanity.com/paper/2109.01902/) (2021)
- [Discovery of New Multi-Level Features for Domain Generalization via Knowledge Corruption](https://mlsanity.com/paper/2109.04320/) (2021)
- [Domain Generalization of Pathological Image Segmentation by Patch-Level and WSI-Level Contrastive Learning](https://mlsanity.com/paper/2508.07539/) (2025)
- [Exploring Data Aggregation and Transformations to Generalize across Visual Domains](https://mlsanity.com/paper/2108.09208/) (2021)
- [Contrastive Learning for Cross-Domain Open World Recognition](https://mlsanity.com/paper/2203.09257/) (2022)
- [Fairness meets Cross-Domain Learning: a new perspective on Models and Metrics](https://mlsanity.com/paper/2303.14411/) (2023)
- [Adaptive Feature Fusion Neural Network for Glaucoma Segmentation on Unseen Fundus Images](https://mlsanity.com/paper/2404.02084/) (2024)
- [TSynD: Targeted Synthetic Data Generation for Enhanced Medical Image Classification](https://mlsanity.com/paper/2406.17473/) (2024)
- [Evaluation of Randomization through Style Transfer for Enhanced Domain Generalization](https://mlsanity.com/paper/2604.05616/) (2026)

## Abstract (arXiv)

Despite being very powerful in standard learning settings, deep learning models can be extremely brittle when deployed in scenarios different from those on which they were trained. Domain generalization methods investigate this problem and data augmentation strategies have shown to be helpful tools to increase data variability, supporting model robustness across domains. In our work we focus on style transfer data augmentation and we present how it can be implemented with a simple and inexpensive strategy to improve generalization. Moreover, we analyze the behavior of current state of the art domain generalization methods when integrated with this augmentation solution: our thorough experimental evaluation shows that their original effect almost always disappears with respect to the augmented baseline. This issue open new scenarios for domain generalization research, highlighting the need of novel methods properly able to take advantage of the introduced data variability.