mlsanityData to 14 Oct 2026

A Style and Semantic Memory Mechanism for Domain Generalization

arXiv 2112.07517 ↗First posted 14 Dec 2021cs.CV cs.AI cs.LGcited by 32

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Abstract (arXiv)

Mainstream state-of-the-art domain generalization algorithms tend to prioritize the assumption on semantic invariance across domains. Meanwhile, the inherent intra-domain style invariance is usually underappreciated and put on the shelf. In this paper, we reveal that leveraging intra-domain style invariance is also of pivotal importance in improving the efficiency of domain generalization. We verify that it is critical for the network to be informative on what domain features are invariant and shared among instances, so that the network sharpens its understanding and improves its semantic discriminative ability. Correspondingly, we also propose a novel "jury" mechanism, which is particularly effective in learning useful semantic feature commonalities among domains. Our complete model called STEAM can be interpreted as a novel probabilistic graphical model, for which the implementation requires convenient constructions of two kinds of memory banks: semantic feature bank and style feature bank. Empirical results show that our proposed framework surpasses the state-of-the-art methods by clear margins.

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). Builds on 47 of its 57 references that are in the map. First posted on arXiv 14 Dec 2021.

Builds on

47 of 57 references.

References · 47

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35 more
  1. 2017Maximum Classifier Discrepancy for Unsupervised Domain Adaptationcited by 683
  2. 2016Deep Transfer Learning with Joint Adaptation Networkscited by 612
  3. 2021Domain Generalization with MixStylecited by 566
  4. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 453
  5. 2019Domain Generalization by Solving Jigsaw Puzzlescited by 450
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  7. 2019Semi-supervised Domain Adaptation via Minimax Entropycited by 335
  8. 2017Unified Deep Supervised Domain Adaptation and Generalizationcited by 323
  9. 2016Domain Separation Networkscited by 320
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  11. 2020Learning to Generate Novel Domains for Domain Generalizationcited by 296
  12. 2018Generalizing Across Domains via Cross-Gradient Trainingcited by 268
  13. 2019Episodic Training for Domain Generalizationcited by 245
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  16. 2017Generate To Adapt: Aligning Domains using Generative Adversarial Networkscited by 183
  17. 2019Learning to Optimize Domain Specific Normalization for Domain Generalizationcited by 161
  18. 2020Domain Adaptive Ensemble Learningcited by 156
  19. 2019Transferrable Prototypical Networks for Unsupervised Domain Adaptationcited by 141
  20. 2019Exploring Object Relation in Mean Teacher for Cross-Domain Detectioncited by 137
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  22. 2020Learning to Balance Specificity and Invariance for In and Out of Domain Generalizationcited by 128
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Cited by year

Citing papers per year: 2022: 11; 2023: 12; 2024: 7; 2025: 2.

32 papers.

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  1. 2022Attention Consistency on Visual Corruptions for Single-Source Domain Generalizationcited by 37
  2. 2023Domain Generalization in Computational Pathology: Survey and Guidelinescited by 29
  3. 2023ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalizationcited by 28
  4. 2023From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modelingcited by 22
  5. 2023MADG: Margin-based Adversarial Learning for Domain Generalizationcited by 22
  6. 2022Causal Balancing for Domain Generalizationcited by 20
  7. 2022Domain-General Crowd Counting in Unseen Scenarioscited by 20
  8. 2023DomainAdaptor: A Novel Approach to Test-time Adaptationcited by 20
  9. 2024Single Domain Generalization for Crowd Countingcited by 17
  10. 2022Domain Generalization -- A Causal Perspectivecited by 14
  11. 2023Modality-Agnostic Debiasing for Single Domain Generalizationcited by 14
  12. 2024Learn to Preserve and Diversify: Parameter-Efficient Group with Orthogonal Regularization for Domain Generalizationcited by 14
20 more
  1. 2022Domain Generalization via Contrastive Causal Learningcited by 8
  2. 2023NormAUG: Normalization-guided Augmentation for Domain Generalizationcited by 8
  3. 2023Causal-DFQ: Causality Guided Data-free Network Quantizationcited by 6
  4. 2023Domain-aware Triplet loss in Domain Generalizationcited by 5
  5. 2024Learning Generalizable Models via Disentangling Spurious and Enhancing Potential Correlationscited by 5
  6. 2022Gated Domain-Invariant Feature Disentanglement for Domain Generalizable Object Detectioncited by 4
  7. 2023Exploring Flat Minima for Domain Generalization with Large Learning Ratescited by 4
  8. 2024A Causal Inspired Early-Branching Structure for Domain Generalizationcited by 4
  9. 2024Feature Diversification and Adaptation for Federated Domain Generalizationcited by 4
  10. 2022Explaining Cross-Domain Recognition with Interpretable Deep Classifiercited by 2
  11. 2025Deconfounding Causal Inference through Two-Branch Framework with Early-Forking for Sensor-Based Cross-Domain Activity Recognitioncited by 2
  12. 2022Memory-Guided Collaborative Attention for Nighttime Thermal Infrared Image Colorizationcited by 1
  13. 2023Parameter Exchange for Robust Dynamic Domain Generalizationcited by 1
  14. 2024DICS: Find Domain-Invariant and Class-Specific Features for Out-of-Distribution Generalizationcited by 1
  15. 2022Out-of-Distribution Detection with Hilbert-Schmidt Independence Optimizationcited by 0
  16. 2022TripleE: Easy Domain Generalization via Episodic Replaycited by 0
  17. 2022Generalization Beyond Feature Alignment: Concept Activation-Guided Contrastive Learningcited by 0
  18. 2023Shape Guided Gradient Voting for Domain Generalizationcited by 0
  19. 2024CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Communitycited by 0
  20. 2025Single Domain Generalization with Adversarial Memorycited by 0