mlsanityData to 14 Oct 2026

Gradual Domain Adaptation via Normalizing Flows

arXiv 2206.11492 ↗First posted 23 Jun 2022stat.ML cs.LGcited by 11

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Abstract (arXiv)

Standard domain adaptation methods do not work well when a large gap exists between the source and target domains. Gradual domain adaptation is one of the approaches used to address the problem. It involves leveraging the intermediate domain, which gradually shifts from the source domain to the target domain. In previous work, it is assumed that the number of intermediate domains is large and the distance between adjacent domains is small; hence, the gradual domain adaptation algorithm, involving self-training with unlabeled datasets, is applicable. In practice, however, gradual self-training will fail because the number of intermediate domains is limited and the distance between adjacent domains is large. We propose the use of normalizing flows to deal with this problem while maintaining the framework of unsupervised domain adaptation. The proposed method learns a transformation from the distribution of the target domain to the Gaussian mixture distribution via the source domain. We evaluate our proposed method by experiments using real-world datasets and confirm that it mitigates the above-explained problem and improves the classification performance.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 54 of its 103 references that are in the map. First posted on arXiv 23 Jun 2022.

Builds on

54 of 103 references.

References · 54

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2018Neural Ordinary Differential Equationscited by 3,779
  4. 2015Variational Inference with Normalizing Flowscited by 2,170
  5. 2016Density estimation using Real NVPcited by 2,042
  6. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,545
  7. 2020WILDS: A Benchmark of in-the-Wild Distribution Shiftscited by 1,375
  8. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,323
  9. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,051
  10. 2018FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Modelscited by 594
  11. 2019PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flowscited by 488
  12. 2020StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flowscited by 349
38 more
  1. 2019Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Designcited by 262
  2. 2020Why Normalizing Flows Fail to Detect Out-of-Distribution Datacited by 221
  3. 2020Fast Differentiable Sorting and Rankingcited by 197
  4. 2018DLOW: Domain Flow for Adaptation and Generalizationcited by 177
  5. 2020Understanding Self-Training for Gradual Domain Adaptationcited by 149
  6. 2020Riemannian Continuous Normalizing Flowscited by 145
  7. 2020OT-Flow: Fast and Accurate Continuous Normalizing Flows via Optimal Transportcited by 143
  8. 2019Progressive Domain Adaptation for Object Detectioncited by 134
  9. 2020Gradually Vanishing Bridge for Adversarial Domain Adaptationcited by 130
  10. 2021Cycle Self-Training for Domain Adaptationcited by 120
  11. 2020Flows for simultaneous manifold learning and density estimationcited by 118
  12. 2020A survey on domain adaptation theory: learning bounds and theoretical guaranteescited by 109
  13. 2020Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximatorscited by 85
  14. 2019Semi-Supervised Learning with Normalizing Flowscited by 76
  15. 2020Continuously Indexed Domain Adaptationcited by 75
  16. 2019VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generationcited by 74
  17. 2021Moser Flow: Divergence-based Generative Modeling on Manifoldscited by 67
  18. 2015A Century of Portraits: A Visual Historical Record of American High School Yearbookscited by 54
  19. 2021IDM: An Intermediate Domain Module for Domain Adaptive Person Re-IDcited by 51
  20. 2019C-Flow: Conditional Generative Flow Models for Images and 3D Point Cloudscited by 50
  21. 2021Rectangular Flows for Manifold Learningcited by 50
  22. 2020Composing Normalizing Flows for Inverse Problemscited by 48
  23. 2020Multi-source Domain Adaptation in the Deep Learning Era: A Systematic Surveycited by 47
  24. 2019Structured Output Learning with Conditional Generative Flowscited by 37
  25. 2020The Expressive Power of a Class of Normalizing Flow Modelscited by 35
  26. 2021KL Guided Domain Adaptationcited by 35
  27. 2019AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flowscited by 33
  28. 2022Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyondcited by 32
  29. 2021Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flowscited by 31
  30. 2021MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generationcited by 22
  31. 2021Gradual Domain Adaptation in the Wild:When Intermediate Distributions are Absentcited by 21
  32. 2020Latent Normalizing Flows for Many-to-Many Cross-Domain Mappingscited by 16
  33. 2021Density estimation on smooth manifolds with normalizing flowscited by 11
  34. 2020Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignmentcited by 10
  35. 2019Temporal Normalizing Flowscited by 8
  36. 2021Online Continual Adaptation with Active Self-Trainingcited by 8
  37. 2021Gradual Domain Adaptation via Self-Training of Auxiliary Modelscited by 8
  38. 2020Accelerating Continuous Normalizing Flow with Trajectory Polynomial Regularizationcited by 6

50 most cited of 54 All 54 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 6; 2026: 2.

11 papers.

  1. 2025Pave Your Own Path: Graph Gradual Domain Adaptation on Fused Gromov-Wasserstein Geodesicscited by 17
  2. 2025Flow-based Domain Randomization for Learning and Sequencing Robotic Skillscited by 4
  3. 2024Gradual Domain Adaptation via Manifold-Constrained Distributionally Robust Optimizationcited by 3
  4. 2024Sharpness-Aware Black-Box Optimizationcited by 2
  5. 2025Gradual Domain Adaptation for Graph Learningcited by 2
  6. 2024When does a bridge become an aeroplane?cited by 1
  7. 2025Self-Training with Dynamic Weighting for Robust Gradual Domain Adaptationcited by 1
  8. 2025SWAT: Sliding Window Adversarial Training for Gradual Domain Adaptationcited by 0
  9. 2025GDO:Gradual Domain Osmosiscited by 0
  10. 2026Rethinking the Flow-Based Gradual Domain Adaptation: A Semi-Dual Optimal Transport Perspectivecited by 0
  11. 2026Transfer learning via interpolating structurescited by 0