Multi-target Domain Adaptation

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

4 leaderboard tables shown for this task, 4 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

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

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

22 shown of 22 papers with code (39 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.

• Unsupervised Domain Adaptation by Backpropagation
  26 Sep 2014 22 repositories listed Syntology ran 34 of 41 samples · 7 unverified · 14 pointer-only (licence)
  Here, we propose a new approach to domain adaptation in deep architectures that can be trained on large amount of labeled data from the source domain and large amount of unlabeled data from the target domain (no labeled…
• Minimum Class Confusion for Versatile Domain Adaptation
  8 Dec 2019 3 repositories listed Syntology ran 1 of 4 samples · 3 unverified · 4 pointer-only (licence)
  It can be characterized as (1) a non-adversarial DA method without explicitly deploying domain alignment, enjoying faster convergence speed; (2) a versatile approach that can handle four existing scenarios: Closed-Set,…
• Merge-Friendly Post-Training Quantization for Multi-Target Domain Adaptation
  29 May 2025 1 repository listed Syntology ran 4 of 6 samples · 2 unverified
  In practical scenarios, quantization is often applied to target-specific data, but this process restricts the domain of interest and introduces discretization effects, making model merging highly non-trivial.
• COSMo: CLIP Talks on Open-Set Multi-Target Domain Adaptation
  31 Aug 2024 1 repository listed
  To the best of our knowledge, COSMo is the first method to address Open-Set Multi-Target DA (OSMTDA), offering a more realistic representation of real-world scenarios and addressing the challenges of both open-set and…
• ConvLoRA and AdaBN based Domain Adaptation via Self-Training
  7 Feb 2024 1 repository listed Syntology ran 2 of 2 samples · 0 unverified
  To further boost adaptation, we utilize Adaptive Batch Normalization (AdaBN) which computes target-specific running statistics and use it along with ConvLoRA.
• MEnsA: Mix-up Ensemble Average for Unsupervised Multi Target Domain Adaptation on 3D Point Clouds
  4 Apr 2023 1 repository listed
  Unsupervised domain adaptation (UDA) addresses the problem of distribution shift between the unlabelled target domain and labelled source domain.
• Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation
  3 Feb 2023 1 repository listed Syntology ran 0 of 4 samples · 4 unverified
  We demonstrate that domain labels are not directly necessary for BTDA if categorical distributions of various domains are sufficiently aligned even facing the imbalance of domains and the label distribution shift of…
• Cyclically Disentangled Feature Translation for Face Anti-spoofing
  7 Dec 2022 1 repository listed
  We further extend CDFTN for multi-target domain adaptation by leveraging data from more unlabeled target domains.
• CoNMix for Source-free Single and Multi-target Domain Adaptation
  7 Nov 2022 1 repository listed Syntology ran 2 of 4 samples · 2 unverified · 4 pointer-only (licence)
  The main motive of this work is to solve for Single and Multi target Domain Adaptation (SMTDA) for the source-free paradigm, which enforces a constraint where the labeled source data is not available during target…
• Cooperative Self-Training for Multi-Target Adaptive Semantic Segmentation
  4 Oct 2022 1 repository listed
  In this work we address multi-target domain adaptation (MTDA) in semantic segmentation, which consists in adapting a single model from an annotated source dataset to multiple unannotated target datasets that differ in…
• A Multi Camera Unsupervised Domain Adaptation Pipeline for Object Detection in Cultural Sites through Adversarial Learning and Self-Training
  3 Oct 2022 1 repository listed
  Object detection algorithms allow to enable many interesting applications which can be implemented in different devices, such as smartphones and wearable devices.
• Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection
  14 Sep 2022 1 repository listed Syntology ran 1 of 3 samples · 2 unverified
  With SEE-VCN, we obtain a unified representation of objects across datasets, allowing the network to focus on learning geometry, rather than overfitting on scan patterns.
• Knowledge Distillation for Multi-Target Domain Adaptation in Real-Time Person Re-Identification
  12 May 2022 1 repository listed
  Despite the recent success of deep learning architectures, person re-identification (ReID) remains a challenging problem in real-word applications.
• ADAS: A Direct Adaptation Strategy for Multi-Target Domain Adaptive Semantic Segmentation
  14 Mar 2022 1 repository listed
  In this paper, we present a direct adaptation strategy (ADAS), which aims to directly adapt a single model to multiple target domains in a semantic segmentation task without pretrained domain-specific models.
• Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain Adaptation
  7 Mar 2022 1 repository listed
  Afterwards, knowledge distillation is performed to iteratively distill different domain knowledge from teachers to a generic student.
• See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation
  17 Nov 2021 1 repository listed
  Sampling discrepancies between different manufacturers and models of lidar sensors result in inconsistent representations of objects.
• Foreground-Aware Stylization and Consensus Pseudo-Labeling for Domain Adaptation of First-Person Hand Segmentation
  6 Jul 2021 1 repository listed
  We validated our method on domain adaptation of hand segmentation from real and simulation images.
• Incremental Multi-Target Domain Adaptation for Object Detection with Efficient Domain Transfer
  13 Apr 2021 1 repository listed
  Recent advances in unsupervised domain adaptation have significantly improved the recognition accuracy of CNNs by alleviating the domain shift between (labeled) source and (unlabeled) target data distributions.
• Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation
  1 Apr 2021 1 repository listed Syntology ran 2 of 6 samples · 4 unverified · 6 pointer-only (licence)
  In this paper we address multi-target domain adaptation (MTDA), where given one labeled source dataset and multiple unlabeled target datasets that differ in data distributions, the task is to learn a robust predictor…
• Unsupervised Multi-Target Domain Adaptation Through Knowledge Distillation
  14 Jul 2020 1 repository listed
  Unsupervised domain adaptation (UDA) seeks to alleviate the problem of domain shift between the distribution of unlabeled data from the target domain w.
• Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks
  8 Jul 2019 1 repository listed
  (Unsupervised) Domain Adaptation (DA) seeks for classifying target instances when solely provided with source labeled and target unlabeled examples for training.
• Domain Agnostic Learning with Disentangled Representations
  28 Apr 2019 1 repository listed
  Unsupervised model transfer has the potential to greatly improve the generalizability of deep models to novel domains.

Syntology lines on 8 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.