Data to 7 Oct 2026

Learning to Generate Novel Domains for Domain Generalization

arXiv 2007.03304 ↗First posted 7 Jul 2020cs.CVcited by 296

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Abstract (arXiv)

This paper focuses on domain generalization (DG), the task of learning from multiple source domains a model that generalizes well to unseen domains. A main challenge for DG is that the available source domains often exhibit limited diversity, hampering the model's ability to learn to generalize. We therefore employ a data generator to synthesize data from pseudo-novel domains to augment the source domains. This explicitly increases the diversity of available training domains and leads to a more generalizable model. To train the generator, we model the distribution divergence between source and synthesized pseudo-novel domains using optimal transport, and maximize the divergence. To ensure that semantics are preserved in the synthesized data, we further impose cycle-consistency and classification losses on the generator. Our method, L2A-OT (Learning to Augment by Optimal Transport) outperforms current state-of-the-art DG methods on four benchmark datasets.

Key facts. Cited by 296 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jul 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 49; 2022: 62; 2023: 74; 2024: 62; 2025: 40; 2026: 9.

296 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
2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
2021Gradient Matching for Domain Generalizationcited by 258
2021RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whiteningcited by 213
2021Learning to Diversify for Single Domain Generalizationcited by 170
2021Deep Stable Learning for Out-Of-Distribution Generalizationcited by 153
2022Federated Learning from Pre-Trained Models: A Contrastive Learning Approachcited by 141
2022Causality Inspired Representation Learning for Domain Generalizationcited by 136

38 more

2022GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Partscited by 135
2022Domain Generalization by Mutual-Information Regularization with Pre-trained Modelscited by 133
2022Uncertainty Modeling for Out-of-Distribution Generalizationcited by 122
2022Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalizationcited by 122
2021OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalizationcited by 86
2021Adversarially Adaptive Normalization for Single Domain Generalizationcited by 85
2022Semantic-Aware Domain Generalized Segmentationcited by 78
2021Generalizable Person Re-identification with Relevance-aware Mixture of Expertscited by 77
2022Improving Test-Time Adaptation via Shift-agnostic Weight Regularization and Nearest Source Prototypescited by 67
2021MixStyle Neural Networks for Domain Generalization and Adaptationcited by 64
2023PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalizationcited by 59
2023Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detectioncited by 57
2021Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorizationcited by 50
2022Pin the Memory: Learning to Generalize Semantic Segmentationcited by 48
2021Style Normalization and Restitution for Domain Generalization and Adaptationcited 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
2021Federated Learning with Domain Generalizationcited by 41
2021Towards Unsupervised Domain Generalizationcited by 40
2021Feature Stylization and Domain-aware Contrastive Learning for Domain Generalizationcited by 40
2022Compound Domain Generalization via Meta-Knowledge Encodingcited by 40
2023StyLIP: Multi-Scale Style-Conditioned Prompt Learning for CLIP-based Domain Generalizationcited by 36
2023What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL?cited by 36
2021Domain Generalization via Inference-time Label-Preserving Target Projectionscited by 35
2022Self-Distilled Vision Transformer for Domain Generalizationcited by 35
2021Feature-based Style Randomization for Domain Generalizationcited by 34
2023Progressive Random Convolutions for Single Domain Generalizationcited by 33
2021A Style and Semantic Memory Mechanism for Domain Generalizationcited by 32
2023Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalizationcited by 31
2024On the Out-Of-Distribution Generalization of Multimodal Large Language Modelscited by 30
2022BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learningcited by 29
2022TeST: Test-time Self-Training under Distribution Shiftcited by 29
2023Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentationcited by 29
2024Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignmentcited by 29
2021NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalizationcited by 28
2023ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalizationcited by 28

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