# Frustratingly Simple Domain Generalization via Image Stylization

arXiv: https://arxiv.org/abs/2006.11207 · HTML: https://mlsanity.com/paper/2006.11207/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2020.

## Built on by (most cited first, 30 of 43)

- [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)
- [Towards Out-Of-Distribution Generalization: A Survey](https://mlsanity.com/paper/2108.13624/) (2021)
- [Parameter-free Online Test-time Adaptation](https://mlsanity.com/paper/2201.05718/) (2022)
- [OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization](https://mlsanity.com/paper/2106.03721/) (2021)
- [Language-Driven Image Style Transfer](https://mlsanity.com/paper/2106.00178/) (2021)
- [Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety](https://mlsanity.com/paper/2104.14235/) (2021)
- [Feature-based Style Randomization for Domain Generalization](https://mlsanity.com/paper/2106.03171/) (2021)
- [A Style and Semantic Memory Mechanism for Domain Generalization](https://mlsanity.com/paper/2112.07517/) (2021)
- [Texture Learning Domain Randomization for Domain Generalized Segmentation](https://mlsanity.com/paper/2303.11546/) (2023)
- [MADG: Margin-based Adversarial Learning for Domain Generalization](https://mlsanity.com/paper/2311.08503/) (2023)
- [From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2310.20271/) (2023)
- [Towards Domain Invariant Single Image Dehazing](https://mlsanity.com/paper/2101.10449/) (2021)
- [Intra-Source Style Augmentation for Improved Domain Generalization](https://mlsanity.com/paper/2210.10175/) (2022)
- [Combining Diverse Feature Priors](https://mlsanity.com/paper/2110.08220/) (2021)
- [Towards Data-Free Domain Generalization](https://mlsanity.com/paper/2110.04545/) (2021)
- [TFS-ViT: Token-Level Feature Stylization for Domain Generalization](https://mlsanity.com/paper/2303.15698/) (2023)
- [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)
- [Intra- & Extra-Source Exemplar-Based Style Synthesis for Improved Domain Generalization](https://mlsanity.com/paper/2307.00648/) (2023)
- [Cross Domain Generative Augmentation: Domain Generalization with Latent Diffusion Models](https://mlsanity.com/paper/2312.05387/) (2023)
- [Robust Domain-Free Domain Generalization with Class-aware Alignment](https://mlsanity.com/paper/2102.08897/) (2021)
- [MResT: Multi-Resolution Sensing for Real-Time Control with Vision-Language Models](https://mlsanity.com/paper/2401.14502/) (2024)
- [FDS: Feedback-guided Domain Synthesis with Multi-Source Conditional Diffusion Models for Domain Generalization](https://mlsanity.com/paper/2407.03588/) (2024)
- [Encouraging Intra-Class Diversity Through a Reverse Contrastive Loss for Better Single-Source Domain Generalization](https://mlsanity.com/paper/2106.07916/) (2021)
- [LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration](https://mlsanity.com/paper/2308.14596/) (2023)
- [INSURE: An Information Theory Inspired Disentanglement and Purification Model for Domain Generalization](https://mlsanity.com/paper/2309.04063/) (2023)
- [Crafting Distribution Shifts for Validation and Training in Single Source Domain Generalization](https://mlsanity.com/paper/2409.19774/) (2024)
- [Domain-Aware Continual Zero-Shot Learning](https://mlsanity.com/paper/2112.12989/) (2021)
- [What's in a Latent? Leveraging Diffusion Latent Space for Domain Generalization](https://mlsanity.com/paper/2503.06698/) (2025)

## Abstract (arXiv)

Convolutional Neural Networks (CNNs) show impressive performance in the standard classification setting where training and testing data are drawn i.i.d. from a given domain. However, CNNs do not readily generalize to new domains with different statistics, a setting that is simple for humans. In this work, we address the Domain Generalization problem, where the classifier must generalize to an unknown target domain. Inspired by recent works that have shown a difference in biases between CNNs and humans, we demonstrate an extremely simple yet effective method, namely correcting this bias by augmenting the dataset with stylized images. In contrast with existing stylization works, which use external data sources such as art, we further introduce a method that is entirely in-domain using no such extra sources of data. We provide a detailed analysis as to the mechanism by which the method works, verifying our claim that it changes the shape/texture bias, and demonstrate results surpassing or comparable to the state of the arts that utilize much more complex methods.