# Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain Validation

arXiv: https://arxiv.org/abs/2011.00444 · HTML: https://mlsanity.com/paper/2011.00444/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2020.

## Built on by (most cited first, 16 of 16)

- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Advances and Challenges in Meta-Learning: A Technical Review](https://mlsanity.com/paper/2307.04722/) (2023)
- [PASTA: Proportional Amplitude Spectrum Training Augmentation for Syn-to-Real Domain Generalization](https://mlsanity.com/paper/2212.00979/) (2022)
- [Domain Generalization through Meta-Learning: A Survey](https://mlsanity.com/paper/2404.02785/) (2024)
- [Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain Scheduler](https://mlsanity.com/paper/2409.17555/) (2024)
- [Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain Generalization](https://mlsanity.com/paper/2412.18342/) (2024)
- [Enhancing Test Time Adaptation with Few-shot Guidance](https://mlsanity.com/paper/2409.01341/) (2024)
- [MLDGG: Meta-Learning for Domain Generalization on Graphs](https://mlsanity.com/paper/2411.12913/) (2024)
- [Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis](https://mlsanity.com/paper/2402.14536/) (2024)
- [Adversarial and Random Transformations for Robust Domain Adaptation and Generalization](https://mlsanity.com/paper/2211.06788/) (2022)
- [Discovering Spatial Relationships by Transformers for Domain Generalization](https://mlsanity.com/paper/2108.10046/) (2021)
- [Dynamically Decoding Source Domain Knowledge for Domain Generalization](https://mlsanity.com/paper/2110.03027/) (2021)
- [ATEAM: Knowledge Integration from Federated Datasets for Vehicle Feature Extraction using Annotation Team of Experts](https://mlsanity.com/paper/2211.09098/) (2022)
- [Fourier Asymmetric Attention on Domain Generalization for Pan-Cancer Drug Response Prediction](https://mlsanity.com/paper/2502.04034/) (2025)
- [NeuRN: Neuro-inspired Domain Generalization for Image Classification](https://mlsanity.com/paper/2505.06881/) (2025)
- [Dual-Path Stable Soft Prompt Generation for Domain Generalization](https://mlsanity.com/paper/2505.18770/) (2025)

## Abstract (arXiv)

The generalization capability of machine learning models, which refers to generalizing the knowledge for an "unseen" domain via learning from one or multiple seen domain(s), is of great importance to develop and deploy machine learning applications in the real-world conditions. Domain Generalization (DG) techniques aim to enhance such generalization capability of machine learning models, where the learnt feature representation and the classifier are two crucial factors to improve generalization and make decisions. In this paper, we propose Discriminative Adversarial Domain Generalization (DADG) with meta-learning-based cross-domain validation. Our proposed framework contains two main components that work synergistically to build a domain-generalized DNN model: (i) discriminative adversarial learning, which proactively learns a generalized feature representation on multiple "seen" domains, and (ii) meta-learning based cross-domain validation, which simulates train/test domain shift via applying meta-learning techniques in the training process. In the experimental evaluation, a comprehensive comparison has been made among our proposed approach and other existing approaches on three benchmark datasets. The results shown that DADG consistently outperforms a strong baseline DeepAll, and outperforms the other existing DG algorithms in most of the evaluation cases.