mlsanityData to 14 Oct 2026

Gradual Domain Adaptation without Indexed Intermediate Domains

arXiv 2207.04587 ↗First posted 11 Jul 2022cs.CV cs.AI cs.LGcited by 26

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Abstract (arXiv)

The effectiveness of unsupervised domain adaptation degrades when there is a large discrepancy between the source and target domains. Gradual domain adaptation (GDA) is one promising way to mitigate such an issue, by leveraging additional unlabeled data that gradually shift from the source to the target. Through sequentially adapting the model along the "indexed" intermediate domains, GDA substantially improves the overall adaptation performance. In practice, however, the extra unlabeled data may not be separated into intermediate domains and indexed properly, limiting the applicability of GDA. In this paper, we investigate how to discover the sequence of intermediate domains when it is not already available. Concretely, we propose a coarse-to-fine framework, which starts with a coarse domain discovery step via progressive domain discriminator training. This coarse domain sequence then undergoes a fine indexing step via a novel cycle-consistency loss, which encourages the next intermediate domain to preserve sufficient discriminative knowledge of the current intermediate domain. The resulting domain sequence can then be used by a GDA algorithm. On benchmark data sets of GDA, we show that our approach, which we name Intermediate DOmain Labeler (IDOL), can lead to comparable or even better adaptation performance compared to the pre-defined domain sequence, making GDA more applicable and robust to the quality of domain sequences. Codes are available at https://github.com/hongyouc/IDOL.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). Builds on 46 of its 101 references that are in the map. First posted on arXiv 11 Jul 2022.

Builds on

46 of 101 references.

References · 46

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34 more
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  7. 2018Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptationcited by 218
  8. 2018A DIRT-T Approach to Unsupervised Domain Adaptationcited by 215
  9. 2018Multi-Adversarial Domain Adaptationcited by 196
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 7; 2024: 6; 2025: 8; 2026: 2.

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  1. 2023Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectivescited by 30
  2. 2022Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptationcited by 28
  3. 2023Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptationcited by 20
  4. 2023Gradual Domain Adaptation: Theory and Algorithmscited by 18
  5. 2025Pave Your Own Path: Graph Gradual Domain Adaptation on Fused Gromov-Wasserstein Geodesicscited by 17
  6. 2023Towards Open-World Segmentation of Partscited by 15
  7. 2023Unified Out-Of-Distribution Detection: A Model-Specific Perspectivecited by 13
  8. 2024Generalizing across Temporal Domains with Koopman Operatorscited by 13
  9. 2022Introducing Intermediate Domains for Effective Self-Training during Test-Timecited by 12
  10. 2023Holistic Transfer: Towards Non-Disruptive Fine-Tuning with Partial Target Datacited by 7
  11. 2024Enhancing Evolving Domain Generalization through Dynamic Latent Representationscited by 6
  12. 2025Progressive Multi-Source Domain Adaptation for Personalized Facial Expression Recognitioncited by 6
14 more
  1. 2023Evolving Domain Adaptation of Pretrained Language Models for Text Classificationcited by 5
  2. 2022DA-CIL: Towards Domain Adaptive Class-Incremental 3D Object Detectioncited by 4
  3. 2024Non-stationary Domain Generalization: Theory and Algorithmcited by 4
  4. 2025Zero Shot Domain Adaptive Semantic Segmentation by Synthetic Data Generation and Progressive Adaptationcited by 3
  5. 2025Gradual Domain Adaptation for Graph Learningcited by 2
  6. 2024Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identificationcited by 1
  7. 2025Incremental Uncertainty-aware Performance Monitoring with Active Labeling Interventioncited by 1
  8. 2025Self-Training with Dynamic Weighting for Robust Gradual Domain Adaptationcited by 1
  9. 2024Stratified Domain Adaptation: A Progressive Self-Training Approach for Scene Text Recognitioncited by 0
  10. 2024Gradual Fine-Tuning with Graph Routing for Multi-Source Unsupervised Domain Adaptationcited by 0
  11. 2025SWAT: Sliding Window Adversarial Training for Gradual Domain Adaptationcited by 0
  12. 2025GDO:Gradual Domain Osmosiscited by 0
  13. 2026Rethinking the Flow-Based Gradual Domain Adaptation: A Semi-Dual Optimal Transport Perspectivecited by 0
  14. 2026Optimal Training-Time Scaling in Gradual Adaptationcited by 0