Multi-Source Unsupervised Domain Adaptation

Benchmarks archive 2025-07-28

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Libraries

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Datasets archive 2025-07-28

6 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

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Most implemented papers archive 2025-07-28

26 shown of 26 papers with code (46 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
  7 Dec 2017 9 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  To solve these problems, we introduce a new approach that attempts to align distributions of source and target by utilizing the task-specific decision boundaries.
• Learning Transferable Features with Deep Adaptation Networks
  10 Feb 2015 5 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation.
• Moment Matching for Multi-Source Domain Adaptation
  4 Dec 2018 3 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  Conventional unsupervised domain adaptation (UDA) assumes that training data are sampled from a single domain.
• Multi-Source Domain Adaptation through Dataset Dictionary Learning in Wasserstein Space
  27 Jul 2023 2 repositories listed
  Based on our dictionary, we propose two novel methods for MSDA: DaDil-R, based on the reconstruction of labeled samples in the target domain, and DaDiL-E, based on the ensembling of classifiers learned on atom…
• Enhancing Domain Adaptation through Prompt Gradient Alignment
  13 Jun 2024 1 repository listed Syntology ran 6 of 12 samples · 6 unverified · 12 pointer-only (licence)
  To tackle this, a line of works based on prompt learning leverages the power of large-scale pre-trained vision-language models to learn both domain-invariant and specific features through a set of domain-agnostic and…
• A Weight-aware-based Multi-source Unsupervised Domain Adaptation Method for Human Motion Intention Recognition
  19 Apr 2024 1 repository listed
  The developed multi-source UDA theory is theoretical and the generalization error on target subject is guaranteed.
• Multi-Source Domain Adaptation for Object Detection with Prototype-based Mean-teacher
  26 Sep 2023 1 repository listed
  Given the use of prototypes, the number of parameters required for our PMT method does not increase significantly with the number of source domains, thus reducing memory issues and possible overfitting.
• Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation
  5 Sep 2023 1 repository listed
  To address such potential distribution shifts, we develop an unsupervised domain adaptation approach that leverages labeled data from multiple source domains and unlabeled data from the target domain.
• Benchmarking Domain Adaptation for Chemical Processes on the Tennessee Eastman Process
  22 Aug 2023 1 repository listed
  In system monitoring, automatic fault diagnosis seeks to infer the systems' state based on sensor readings, e.
• MS3D++: Ensemble of Experts for Multi-Source Unsupervised Domain Adaption in 3D Object Detection
  11 Aug 2023 1 repository listed
  MS3D++ provides a straightforward approach to domain adaptation by generating high-quality pseudo-labels, enabling the adaptation of 3D detectors to a diverse range of lidar types, regardless of their density.
• FACT: Federated Adversarial Cross Training
  1 Jun 2023 1 repository listed
  We propose Federated Adversarial Cross Training (FACT), which uses the implicit domain differences between source clients to identify domain shifts in the target domain.
• Multi-Prompt Alignment for Multi-Source Unsupervised Domain Adaptation
  30 Sep 2022 1 repository listed Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
  Most existing methods for unsupervised domain adaptation (UDA) rely on a shared network to extract domain-invariant features.
• Aligning Domain-specific Distribution and Classifier for Cross-domain Classification from Multiple Sources
  4 Jan 2022 1 repository listed Syntology ran 1 of 4 samples · 3 unverified
  However, in the practical scenario, labeled data can be typically collected from multiple diverse sources, and they might be different not only from the target domain but also from each other.
• Seeking Similarities over Differences: Similarity-based Domain Alignment for Adaptive Object Detection
  4 Oct 2021 1 repository listed
  In order to robustly deploy object detectors across a wide range of scenarios, they should be adaptable to shifts in the input distribution without the need to constantly annotate new data.
• Improving Transferability of Domain Adaptation Networks Through Domain Alignment Layers
  6 Sep 2021 1 repository listed
  Deep learning (DL) has been the primary approach used in various computer vision tasks due to its relevant results achieved on many tasks.
• Secure Domain Adaptation with Multiple Sources
  23 Jun 2021 1 repository listed
  Multi-source unsupervised domain adaptation (MUDA) is a framework to address the challenge of annotated data scarcity in a target domain via transferring knowledge from multiple annotated source domains.
• Wasserstein Barycenter for Multi-Source Domain Adaptation
  19 Jun 2021 1 repository listed
  To overcome the challenges posed by this learning scenario, we propose a method for constructing an intermediate domain between sources and target domain, the Wasserstein Barycenter Transport (WBT).
• MOST: Multi-Source Domain Adaptation via Optimal Transport for Student-Teacher Learning
  13 May 2021 1 repository listed
  To this end, we propose in this paper a novel model for multi-source DA using the theory of optimal transport and imitation learning.
• STEM: An Approach to Multi-Source Domain Adaptation With Guarantees
  1 Jan 2021 1 repository listed
  To address the second challenge, we propose to bridge the gap between the target domain and the mixture of source domains in the latent space via a generator or feature extractor.
• KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation
  19 Nov 2020 1 repository listed
  (2) A dynamic weighting strategy named Consensus Focus to identify both the malicious and irrelevant domains.
• Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation
  17 Jul 2020 1 repository listed
  Transferring knowledges learned from multiple source domains to target domain is a more practical and challenging task than conventional single-source domain adaptation.
• Multi-source Attention for Unsupervised Domain Adaptation
  14 Apr 2020 1 repository listed
  We model source-selection as an attention-learning problem, where we learn attention over sources for a given target instance.
• Domain Adaptive Ensemble Learning
  16 Mar 2020 1 repository listed
  Each such classifier is an expert to its own domain and a non-expert to others.
• Multi-source Distilling Domain Adaptation
  22 Nov 2019 1 repository listed
  Deep neural networks suffer from performance decay when there is domain shift between the labeled source domain and unlabeled target domain, which motivates the research on domain adaptation (DA).
• Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift
  2 Mar 2018 1 repository listed
  Motivated by the theoretical results in \cite{mansour2009domain}, the target distribution can be represented as the weighted combination of source distributions, and, the multi-source unsupervised domain adaptation via…
• Deep Transfer Learning with Joint Adaptation Networks
  21 May 2016 1 repository listed
  Deep networks have been successfully applied to learn transferable features for adapting models from a source domain to a different target domain.

Syntology lines on 6 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.