Data to 7 Oct 2026

Deep Domain-Adversarial Image Generation for Domain Generalisation

arXiv 2003.06054 ↗First posted 12 Mar 2020cs.CVcited by 231

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Abstract (arXiv)

Machine learning models typically suffer from the domain shift problem when trained on a source dataset and evaluated on a target dataset of different distribution. To overcome this problem, domain generalisation (DG) methods aim to leverage data from multiple source domains so that a trained model can generalise to unseen domains. In this paper, we propose a novel DG approach based on Deep Domain-Adversarial Image Generation (DDAIG). Specifically, DDAIG consists of three components, namely a label classifier, a domain classifier and a domain transformation network (DoTNet). The goal for DoTNet is to map the source training data to unseen domains. This is achieved by having a learning objective formulated to ensure that the generated data can be correctly classified by the label classifier while fooling the domain classifier. By augmenting the source training data with the generated unseen domain data, we can make the label classifier more robust to unknown domain changes. Extensive experiments on four DG datasets demonstrate the effectiveness of our approach.

Key facts. Cited by 231 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Mar 2020.

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Citing papers per year: 2021: 37; 2022: 54; 2023: 63; 2024: 38; 2025: 31; 2026: 8.

231 papers.

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2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 762
2021Domain Generalization: A Surveycited by 760
2021Domain Generalization with MixStylecited by 563
2021Towards Out-Of-Distribution Generalization: A Surveycited by 431
2021A Fourier-based Framework for Domain Generalizationcited by 287
2022Improving Out-of-Distribution Robustness via Selective Augmentationcited by 202
2022Parameter-free Online Test-time Adaptationcited by 184
2021A Fine-Grained Analysis on Distribution Shiftcited by 171
2021Deep Stable Learning for Out-Of-Distribution Generalizationcited by 153
2022Causality Inspired Representation Learning for Domain Generalizationcited by 136
2023Diffusion Models for Image Restoration and Enhancement: A Comprehensive Surveycited by 108
2021Model-Based Domain Generalizationcited by 103

38 more

2021OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalizationcited by 86
2022NICO++: Towards Better Benchmarking for Domain Generalizationcited by 86
2022Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Timecited by 85
2023Differentially Private Diffusion Models Generate Useful Synthetic Imagescited by 85
2021MixStyle Neural Networks for Domain Generalization and Adaptationcited by 64
2023PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalizationcited by 59
2022C-Mixup: Improving Generalization in Regressioncited by 57
2022DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotationscited by 54
2023SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalizationcited by 48
2021Domain Adversarial Neural Networks for Domain Generalization: When It Works and How to Improvecited by 46
2021Generalizable Cross-modality Medical Image Segmentation via Style Augmentation and Dual Normalizationcited by 45
2022Out-of-distribution Generalization with Causal Invariant Transformationscited by 45
2021Semi-Supervised Domain Generalization with Stochastic StyleMatchcited by 43
2023Meta-causal Learning for Single Domain Generalizationcited by 43
2022Deep Frequency Filtering for Domain Generalizationcited by 41
2021Towards Unsupervised Domain Generalizationcited by 40
2022Compound Domain Generalization via Meta-Knowledge Encodingcited by 40
2023A Sentence Speaks a Thousand Images: Domain Generalization through Distilling CLIP with Language Guidancecited by 40
2021Domain Prompt Learning for Efficiently Adapting CLIP to Unseen Domainscited by 39
2023StyLIP: Multi-Scale Style-Conditioned Prompt Learning for CLIP-based Domain Generalizationcited by 36
2021Domain Generalization via Inference-time Label-Preserving Target Projectionscited by 35
2021Feature-based Style Randomization for Domain Generalizationcited by 34
2022Domain-invariant Feature Exploration for Domain Generalizationcited by 34
2023Progressive Random Convolutions for Single Domain Generalizationcited by 33
2024On the Out-Of-Distribution Generalization of Multimodal Large Language Modelscited by 30
2021Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillationcited by 28
2023ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalizationcited by 28
2023SRCD: Semantic Reasoning with Compound Domains for Single-Domain Generalized Object Detectioncited by 28
2023Flatness-Aware Minimization for Domain Generalizationcited by 27
2023A Simple Recipe for Language-guided Domain Generalized Segmentationcited by 27
2021Rethinking Domain Generalization Baselinescited by 26
2022I Can't Believe There's No Images! Learning Visual Tasks Using only Language Supervisioncited by 26
2022Test-time Fourier Style Calibration for Domain Generalizationcited by 25
2022Single-source Domain Expansion Network for Cross-Scene Hyperspectral Image Classificationcited by 25
2023Out-of-Domain Robustness via Targeted Augmentationscited by 24
2023Multi-view Adversarial Discriminator: Mine the Non-causal Factors for Object Detection in Unseen Domainscited by 24
2023Generalizable Decision Boundaries: Dualistic Meta-Learning for Open Set Domain Generalizationcited by 24
2024Prompt-Driven Dynamic Object-Centric Learning for Single Domain Generalizationcited by 24

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