# Domain Generalization through Meta-Learning: A Survey

arXiv: https://arxiv.org/abs/2404.02785 · HTML: https://mlsanity.com/paper/2404.02785/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 67 of its 113 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 30 of 67)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [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)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Fine-Grained Visual Classification of Aircraft](https://mlsanity.com/paper/1306.5151/) (2013)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [In Search of Lost Domain Generalization](https://mlsanity.com/paper/2007.01434/) (2020)
- [Deeper, Broader and Artier Domain Generalization](https://mlsanity.com/paper/1710.03077/) (2017)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Multi-Task Learning as Multi-Objective Optimization](https://mlsanity.com/paper/1810.04650/) (2018)
- [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)
- [Large Scale Incremental Learning](https://mlsanity.com/paper/1905.13260/) (2019)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [Domain Generalization via Invariant Feature Representation](https://mlsanity.com/paper/1301.2115/) (2013)
- [Deep Domain Confusion: Maximizing for Domain Invariance](https://mlsanity.com/paper/1412.3474/) (2014)
- [End-to-End Incremental Learning](https://mlsanity.com/paper/1807.09536/) (2018)
- [Learning multiple visual domains with residual adapters](https://mlsanity.com/paper/1705.08045/) (2017)
- [Generalizing to Unseen Domains via Adversarial Data Augmentation](https://mlsanity.com/paper/1805.12018/) (2018)
- [Towards Out-Of-Distribution Generalization: A Survey](https://mlsanity.com/paper/2108.13624/) (2021)
- [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)
- [Domain Separation Networks](https://mlsanity.com/paper/1608.06019/) (2016)
- [Learning to Learn Single Domain Generalization](https://mlsanity.com/paper/2003.13216/) (2020)
- [Learning to Generate Novel Domains for Domain Generalization](https://mlsanity.com/paper/2007.03304/) (2020)

## Built on by (most cited first, 28 of 28)

- [Out-of-Distribution Generalization in Time Series: A Survey](https://mlsanity.com/paper/2503.13868/) (2025)
- [CLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive Survey](https://mlsanity.com/paper/2504.14280/) (2025)
- [Learning Robust Spectral Dynamics for Temporal Domain Generalization](https://mlsanity.com/paper/2505.12585/) (2025)
- [Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigation](https://mlsanity.com/paper/2510.08962/) (2025)
- [Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges](https://mlsanity.com/paper/2507.02074/) (2025)
- [Let Synthetic Data Shine: Domain Reassembly and Soft-Fusion for Single Domain Generalization](https://mlsanity.com/paper/2503.13617/) (2025)
- [A Survey on Training-free Alignment of Large Language Models](https://mlsanity.com/paper/2508.09016/) (2025)
- [Meta Curvature-Aware Minimization for Domain Generalization](https://mlsanity.com/paper/2412.11542/) (2024)
- [GREAT: Generalizable Representation Enhancement via Auxiliary Transformations for Zero-Shot Environmental Prediction](https://mlsanity.com/paper/2511.13469/) (2025)
- [Generalizing Vision-Language Models with Dedicated Prompt Guidance](https://mlsanity.com/paper/2512.02421/) (2025)
- [Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning](https://mlsanity.com/paper/2605.08839/) (2026)
- [GEN-Guard: Correcting Generalization Failures for Deployable Federated Surgical AI](https://mlsanity.com/paper/2606.20303/) (2026)
- [Domain penalisation for improved Out-of-Distribution Generalisation](https://mlsanity.com/paper/2408.01746/) (2024)
- [Robust Predictive Modeling Under Unseen Data Distribution Shifts: A Methodological Commentary](https://mlsanity.com/paper/2503.03399/) (2025)
- [FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition](https://mlsanity.com/paper/2503.18998/) (2025)
- [MMformer with Adaptive Transferable Attention: Advancing Multivariate Time Series Forecasting for Environmental Applications](https://mlsanity.com/paper/2504.14050/) (2025)
- [Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction](https://mlsanity.com/paper/2504.17655/) (2025)
- [SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images](https://mlsanity.com/paper/2509.02287/) (2025)
- [Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2509.08570/) (2025)
- [Gains: Fine-grained Federated Domain Adaptation in Open Set](https://mlsanity.com/paper/2510.15967/) (2025)
- [AdvBlur: Adversarial Blur for Robust Diabetic Retinopathy Classification and Cross-Domain Generalization](https://mlsanity.com/paper/2510.24000/) (2025)
- [Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules versus Therapeutic Peptides](https://mlsanity.com/paper/2511.00209/) (2025)
- [HydroDCM: Hydrological Domain-Conditioned Modulation for Cross-Reservoir Inflow Prediction](https://mlsanity.com/paper/2512.03300/) (2025)
- [Meta-learning to Address Data Shift in Time Series Classification](https://mlsanity.com/paper/2601.09018/) (2026)
- [Looking Beyond Accuracy: A Holistic Benchmark of ECG Foundation Models](https://mlsanity.com/paper/2601.21830/) (2026)
- [Domain-Invariant Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2603.28555/) (2026)
- [Early Prediction of Future Behavioral Strategy from Process Traces](https://mlsanity.com/paper/2605.30550/) (2026)
- [Vision: Data-Centric Anchoring for Robust and Interpretable Agentic AI](https://mlsanity.com/paper/2609.08216/) (2026)

## Abstract (arXiv)

Deep neural networks (DNNs) have revolutionized artificial intelligence but often lack performance when faced with out-of-distribution (OOD) data, a common scenario due to the inevitable domain shifts in real-world applications. This limitation stems from the common assumption that training and testing data share the same distribution--an assumption frequently violated in practice. Despite their effectiveness with large amounts of data and computational power, DNNs struggle with distributional shifts and limited labeled data, leading to overfitting and poor generalization across various tasks and domains. Meta-learning presents a promising approach by employing algorithms that acquire transferable knowledge across various tasks for fast adaptation, eliminating the need to learn each task from scratch. This survey paper delves into the realm of meta-learning with a focus on its contribution to domain generalization. We first clarify the concept of meta-learning for domain generalization and introduce a novel taxonomy based on the feature extraction strategy and the classifier learning methodology, offering a granular view of methodologies. Additionally, we present a decision graph to assist readers in navigating the taxonomy based on data availability and domain shifts, enabling them to select and develop a proper model tailored to their specific problem requirements. Through an exhaustive review of existing methods and underlying theories, we map out the fundamentals of the field. Our survey provides practical insights and an informed discussion on promising research directions.