mlsanityData to 7 Oct 2026

Deep Hashing Network for Unsupervised Domain Adaptation

arXiv 1706.07522 ↗First posted 22 Jun 2017cs.CVcited by 1,181

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Cited by 46 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In recent years, deep neural networks have emerged as a dominant machine learning tool for a wide variety of application domains. However, training a deep neural network requires a large amount of labeled data, which is an expensive process in terms of time, labor and human expertise. Domain adaptation or transfer learning algorithms address this challenge by leveraging labeled data in a different, but related source domain, to develop a model for the target domain. Further, the explosive growth of digital data has posed a fundamental challenge concerning its storage and retrieval. Due to its storage and retrieval efficiency, recent years have witnessed a wide application of hashing in a variety of computer vision applications. In this paper, we first introduce a new dataset, Office-Home, to evaluate domain adaptation algorithms. The dataset contains images of a variety of everyday objects from multiple domains. We then propose a novel deep learning framework that can exploit labeled source data and unlabeled target data to learn informative hash codes, to accurately classify unseen target data. To the best of our knowledge, this is the first research effort to exploit the feature learning capabilities of deep neural networks to learn representative hash codes to address the domain adaptation problem. Our extensive empirical studies on multiple transfer tasks corroborate the usefulness of the framework in learning efficient hash codes which outperform existing competitive baselines for unsupervised domain adaptation.

Key facts. Cited by 1,181 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jun 2017.

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Citing papers per year: 2021: 205; 2022: 214; 2023: 258; 2024: 229; 2025: 179; 2026: 96.

1,181 papers.

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  1. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,550
  2. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 762
  3. 2021Domain Generalization: A Surveycited by 760
  4. 2021Domain Generalization with MixStylecited by 563
  5. 2021SWAD: Domain Generalization by Seeking Flat Minimacited by 431
  6. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 431
  7. 2021A Fourier-based Framework for Domain Generalizationcited by 287
  8. 2021LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentationcited by 259
  9. 2021Gradient Matching for Domain Generalizationcited by 258
  10. 2021SelfReg: Self-supervised Contrastive Regularization for Domain Generalizationcited by 189
  11. 2021Fishr: Invariant Gradient Variances for Out-of-Distribution Generalizationcited by 186
  12. 2023Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Needcited by 183
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  1. 2021Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptationcited by 176
  2. 2022MIC: Masked Image Consistency for Context-Enhanced Domain Adaptationcited by 176
  3. 2021Generalized Source-free Domain Adaptationcited by 175
  4. 2021A Fine-Grained Analysis on Distribution Shiftcited by 171
  5. 2022Diverse Weight Averaging for Out-of-Distribution Generalizationcited by 148
  6. 2022SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptationcited by 146
  7. 2021Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalizationcited by 144
  8. 2022Causality Inspired Representation Learning for Domain Generalizationcited by 136
  9. 2022Domain Generalization by Mutual-Information Regularization with Pre-trained Modelscited by 133
  10. 2021CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptationcited by 129
  11. 2022Domain Adaptation via Prompt Learningcited by 128
  12. 2021Unbalanced minibatch Optimal Transport; applications to Domain Adaptationcited by 123
  13. 2022Uncertainty Modeling for Out-of-Distribution Generalizationcited by 122
  14. 2021Cycle Self-Training for Domain Adaptationcited by 120
  15. 2021Deep Subdomain Adaptation Network for Image Classificationcited by 119
  16. 2021Open Domain Generalization with Domain-Augmented Meta-Learningcited by 115
  17. 2021Source-free Domain Adaptation via Avatar Prototype Generation and Adaptationcited by 111
  18. 2023A Comprehensive Survey on Source-free Domain Adaptationcited by 104
  19. 2023Sharpness-Aware Gradient Matching for Domain Generalizationcited by 102
  20. 2021Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learningcited by 100
  21. 2022Source-Free Domain Adaptation via Distribution Estimationcited by 97
  22. 2022Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalizationcited by 97
  23. 2023CLIPood: Generalizing CLIP to Out-of-Distributionscited by 96
  24. 2021Towards a Theoretical Framework of Out-of-Distribution Generalizationcited by 94
  25. 2022Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptationcited by 93
  26. 2021OVANet: One-vs-All Network for Universal Domain Adaptationcited by 92
  27. 2021OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalizationcited by 86
  28. 2021Invariant Information Bottleneck for Domain Generalizationcited by 86
  29. 2021Exploiting Domain-Specific Features to Enhance Domain Generalizationcited by 86
  30. 2022NICO++: Towards Better Benchmarking for Domain Generalizationcited by 86
  31. 2021Extending the WILDS Benchmark for Unsupervised Adaptationcited by 85
  32. 2022Sparse Mixture-of-Experts are Domain Generalizable Learnerscited by 85
  33. 2022A Closer Look at Smoothness in Domain Adversarial Trainingcited by 85
  34. 2022Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Timecited by 85
  35. 2021Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptationcited by 83
  36. 2021TVT: Transferable Vision Transformer for Unsupervised Domain Adaptationcited by 81
  37. 2022MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflictscited by 81
  38. 2022Source-Free Unsupervised Domain Adaptation: A Surveycited by 79

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