mlsanityData to 7 Oct 2026

Domain Generalization by Learning and Removing Domain-specific Features

arXiv 2212.07101 ↗First posted 14 Dec 2022cs.CV cs.LGcited by 28

Not on the map. Why?

Abstract (arXiv)

Deep Neural Networks (DNNs) suffer from domain shift when the test dataset follows a distribution different from the training dataset. Domain generalization aims to tackle this issue by learning a model that can generalize to unseen domains. In this paper, we propose a new approach that aims to explicitly remove domain-specific features for domain generalization. Following this approach, we propose a novel framework called Learning and Removing Domain-specific features for Generalization (LRDG) that learns a domain-invariant model by tactically removing domain-specific features from the input images. Specifically, we design a classifier to effectively learn the domain-specific features for each source domain, respectively. We then develop an encoder-decoder network to map each input image into a new image space where the learned domain-specific features are removed. With the images output by the encoder-decoder network, another classifier is designed to learn the domain-invariant features to conduct image classification. Extensive experiments demonstrate that our framework achieves superior performance compared with state-of-the-art methods.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Builds on 36 of its 51 references that are in the map. First posted on arXiv 14 Dec 2022.

Builds on

36 of 51 references.

References · 36

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  5. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  6. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  7. 2020Shortcut Learning in Deep Neural Networkscited by 1,928
  8. 2019Invariant Risk Minimizationcited by 1,832
  9. 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,430
  10. 2017Deep Hashing Network for Unsupervised Domain Adaptationcited by 1,181
  11. 2019Learning Robust Global Representations by Penalizing Local Predictive Powercited by 1,010
  12. 2014Striving for Simplicity: The All Convolutional Netcited by 983
24 more
  1. 2017Deeper, Broader and Artier Domain Generalizationcited by 983
  2. 2013Domain Generalization via Invariant Feature Representationcited by 622
  3. 2021Domain Generalization with MixStylecited by 563
  4. 2019Domain Generalization by Solving Jigsaw Puzzlescited by 449
  5. 2021SWAD: Domain Generalization by Seeking Flat Minimacited by 431
  6. 2020Self-Challenging Improves Cross-Domain Generalizationcited by 426
  7. 2019Domain Generalization via Model-Agnostic Learning of Semantic Featurescited by 405
  8. 2015Domain Generalization for Object Recognition with Multi-task Autoencoderscited by 253
  9. 2019Episodic Training for Domain Generalizationcited by 245
  10. 2020Domain Generalization using Causal Matchingcited by 224
  11. 2021Fishr: Invariant Gradient Variances for Out-of-Distribution Generalizationcited by 186
  12. 2019Learning to Optimize Domain Specific Normalization for Domain Generalizationcited by 160
  13. 2021Deep Stable Learning for Out-Of-Distribution Generalizationcited by 153
  14. 2020Robust and Generalizable Visual Representation Learning via Random Convolutionscited by 141
  15. 2020Learning to Balance Specificity and Invariance for In and Out of Domain Generalizationcited by 128
  16. 2019Learning Robust Representations by Projecting Superficial Statistics Outcited by 123
  17. 2019Generalizing to unseen domains via distribution matchingcited by 109
  18. 2020Learning to Learn with Variational Information Bottleneck for Domain Generalizationcited by 102
  19. 2017Measuring the tendency of CNNs to Learn Surface Statistical Regularitiescited by 87
  20. 2021Exploiting Domain-Specific Features to Enhance Domain Generalizationcited by 86
  21. 2014Domain-Adversarial Neural Networkscited by 78
  22. 2018Domain Generalization with Domain-Specific Aggregation Modulescited by 61
  23. 2020Automatic Shortcut Removal for Self-Supervised Representation Learningcited by 43
  24. 2021Rethinking Domain Generalization Baselinescited by 26

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 9; 2024: 7; 2025: 10; 2026: 2.

28 papers.

Sort:

By area:

  1. 2023PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalizationcited by 59
  2. 2023A Sentence Speaks a Thousand Images: Domain Generalization through Distilling CLIP with Language Guidancecited by 40
  3. 2023DomainDrop: Suppressing Domain-Sensitive Channels for Domain Generalizationcited by 21
  4. 2023NormAUG: Normalization-guided Augmentation for Domain Generalizationcited by 8
  5. 2025E-BATS: Efficient Backpropagation-Free Test-Time Adaptation for Speech Foundation Modelscited by 8
  6. 2023Generalizing to Unseen Domains in Diabetic Retinopathy Classificationcited by 7
  7. 2024Point-PRC: A Prompt Learning Based Regulation Framework for Generalizable Point Cloud Analysiscited by 6
  8. 2024Exclusive Style Removal for Cross Domain Novel Class Discoverycited by 5
  9. 2024Hybrid-TTA: Continual Test-time Adaptation via Dynamic Domain Shift Detectioncited by 5
  10. 2024START: A Generalized State Space Model with Saliency-Driven Token-Aware Transformationcited by 4
  11. 2023A Novel Cross-Perturbation for Single Domain Generalizationcited by 3
  12. 2025Generalizable Multispectral Land Cover Classification via Frequency-Aware Mixture of Low-Rank Token Expertscited by 3
16 more
  1. 2025Discovering Hierarchy-Grounded Domains with Adaptive Granularity for Clinical Domain Generalizationcited by 3
  2. 2025Pruning by Block Benefit: Exploring the Properties of Vision Transformer Blocks during Domain Adaptationcited by 3
  3. 2024Domain Generalizable Knowledge Tracing via Concept Aggregation and Relation-Based Attentioncited by 2
  4. 2025Unlocking the Potential of Unlabeled Data in Semi-Supervised Domain Generalizationcited by 2
  5. 2024Reliable OOD Virtual Screening with Extrapolatory Pseudo-Label Matchingcited by 1
  6. 2025Control-CLIP: Decoupling Category and Style Guidance in CLIP for Specific-Domain Generationcited by 1
  7. 2025MEASURE: Multi-scale Minimal Sufficient Representation Learning for Domain Generalization in Sleep Stagingcited by 1
  8. 2026UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shiftscited by 1
  9. 2023Patch-aware Batch Normalization for Improving Cross-domain Robustnesscited by 0
  10. 2023Road Disease Detection based on Latent Domain Background Feature Separation and Suppressioncited by 0
  11. 2023Choosing Wisely and Learning Deeply: Selective Cross-Modality Distillation via CLIP for Domain Generalizationcited by 0
  12. 2024IMO: Greedy Layer-Wise Sparse Representation Learning for Out-of-Distribution Text Classification with Pre-trained Modelscited by 0
  13. 2025Casual Inference via Style Bias Deconfounding for Domain Generalizationcited by 0
  14. 2025Incorporating Interventional Independence Improves Robustness against Interventional Distribution Shiftcited by 0
  15. 2025SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view imagescited by 0
  16. 2026Two-Valued Symmetric Circulant Matrices: Applications in Deep Learningcited by 0