# Domain Generalization via Frequency-domain-based Feature Disentanglement and Interaction

arXiv: https://arxiv.org/abs/2201.08029 · HTML: https://mlsanity.com/paper/2201.08029/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 51 references that are in the map.
- First posted on arXiv 20 Jan 2022.

## Builds on (most cited first, 30 of 37)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Moment Matching for Multi-Source Domain Adaptation](https://mlsanity.com/paper/1812.01754/) (2018)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [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 with MixStyle](https://mlsanity.com/paper/2104.02008/) (2021)
- [FDA: Fourier Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2004.05498/) (2020)
- [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)
- [Domain Generalization via Model-Agnostic Learning of Semantic Features](https://mlsanity.com/paper/1910.13580/) (2019)
- [Unified Deep Supervised Domain Adaptation and Generalization](https://mlsanity.com/paper/1709.10190/) (2017)
- [Learning to Generate Novel Domains for Domain Generalization](https://mlsanity.com/paper/2007.03304/) (2020)
- [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)
- [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)
- [Bilinear CNNs for Fine-grained Visual Recognition](https://mlsanity.com/paper/1504.07889/) (2015)
- [Domain Generalization using Causal Matching](https://mlsanity.com/paper/2006.07500/) (2020)
- [Domain Generalization Using a Mixture of Multiple Latent Domains](https://mlsanity.com/paper/1911.07661/) (2019)
- [FSDR: Frequency Space Domain Randomization for Domain Generalization](https://mlsanity.com/paper/2103.02370/) (2021)
- [Efficient Domain Generalization via Common-Specific Low-Rank Decomposition](https://mlsanity.com/paper/2003.12815/) (2020)
- [Domain Agnostic Learning with Disentangled Representations](https://mlsanity.com/paper/1904.12347/) (2019)
- [Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization](https://mlsanity.com/paper/2007.09316/) (2020)
- [Cross-domain Face Presentation Attack Detection via Multi-domain Disentangled Representation Learning](https://mlsanity.com/paper/2004.01959/) (2020)
- [Visual Domain Adaptation with Manifold Embedded Distribution Alignment](https://mlsanity.com/paper/1807.07258/) (2018)
- [Continuously Indexed Domain Adaptation](https://mlsanity.com/paper/2007.01807/) (2020)

## Built on by (most cited first, 21 of 21)

- [PASTA: Proportional Amplitude Spectrum Training Augmentation for Syn-to-Real Domain Generalization](https://mlsanity.com/paper/2212.00979/) (2022)
- [ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalization](https://mlsanity.com/paper/2303.11674/) (2023)
- [Decompose, Adjust, Compose: Effective Normalization by Playing with Frequency for Domain Generalization](https://mlsanity.com/paper/2303.02328/) (2023)
- [Learning Spectral-Decomposed Tokens for Domain Generalized Semantic Segmentation](https://mlsanity.com/paper/2407.18568/) (2024)
- [MoreStyle: Relax Low-frequency Constraint of Fourier-based Image Reconstruction in Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2403.11689/) (2024)
- [Towards Combating Frequency Simplicity-biased Learning for Domain Generalization](https://mlsanity.com/paper/2410.16146/) (2024)
- [Resolve Domain Conflicts for Generalizable Remote Physiological Measurement](https://mlsanity.com/paper/2404.07855/) (2024)
- [Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching](https://mlsanity.com/paper/2506.12413/) (2025)
- [SETA: Semantic-Aware Token Augmentation for Domain Generalization](https://mlsanity.com/paper/2403.11792/) (2024)
- [ED-SAM: An Efficient Diffusion Sampling Approach to Domain Generalization in Vision-Language Foundation Models](https://mlsanity.com/paper/2406.01432/) (2024)
- [PointDGMamba: Domain Generalization of Point Cloud Classification via Generalized State Space Model](https://mlsanity.com/paper/2408.13574/) (2024)
- [Split Adaptation for Pre-trained Vision Transformers](https://mlsanity.com/paper/2503.00441/) (2025)
- [Frequency Regularization: Restricting Information Redundancy of Convolutional Neural Networks](https://mlsanity.com/paper/2304.07973/) (2023)
- [Calibration-based Dual Prototypical Contrastive Learning Approach for Domain Generalization Semantic Segmentation](https://mlsanity.com/paper/2309.14282/) (2023)
- [ConDiSR: Contrastive Disentanglement and Style Regularization for Single Domain Generalization](https://mlsanity.com/paper/2403.09400/) (2024)
- [Dual-stream Feature Augmentation for Domain Generalization](https://mlsanity.com/paper/2409.04699/) (2024)
- [FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching](https://mlsanity.com/paper/2506.08518/) (2025)
- [Leveraging Prior Knowledge of Diffusion Model for Person Search](https://mlsanity.com/paper/2510.01841/) (2025)
- [Progressive Multi-Level Alignments for Semi-Supervised Domain Adaptation SAR Target Recognition Using Simulated Data](https://mlsanity.com/paper/2411.04711/) (2024)
- [Generative Classifier for Domain Generalization](https://mlsanity.com/paper/2504.02272/) (2025)
- [LiteMatch: Lightweight Zero-Shot Stereo Matching via Cost Volume Stabilization](https://mlsanity.com/paper/2606.31636/) (2026)

## Abstract (arXiv)

Adaptation to out-of-distribution data is a meta-challenge for all statistical learning algorithms that strongly rely on the i.i.d. assumption. It leads to unavoidable labor costs and confidence crises in realistic applications. For that, domain generalization aims at mining domain-irrelevant knowledge from multiple source domains that can generalize to unseen target domains. In this paper, by leveraging the frequency domain of an image, we uniquely work with two key observations: (i) the high-frequency information of an image depicts object edge structure, which preserves high-level semantic information of the object is naturally consistent across different domains, and (ii) the low-frequency component retains object smooth structure, while this information is susceptible to domain shifts. Motivated by the above observations, we introduce (i) an encoder-decoder structure to disentangle high- and low-frequency feature of an image, (ii) an information interaction mechanism to ensure the helpful knowledge from both two parts can cooperate effectively, and (iii) a novel data augmentation technique that works on the frequency domain to encourage the robustness of frequency-wise feature disentangling. The proposed method obtains state-of-the-art performance on three widely used domain generalization benchmarks (Digit-DG, Office-Home, and PACS).