Domain Adaptation

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

58 leaderboard tables shown for this task, 58 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. 10 shown of 58 until expanded.

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

96 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 96 until expanded.

Subtasks archive 2025-07-28

13 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 2,400 papers with code (6,439 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.

• Language Models are Few-Shot Learners
  28 May 2020 67 repositories listed Syntology ran 15 of 65 samples · 50 unverified · 4 pointer-only (licence)
  By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
• Domain-Adversarial Training of Neural Networks
  28 May 2015 37 repositories listed Syntology ran 33 of 52 samples · 19 unverified · 22 pointer-only (licence)
  Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the…
• SuperPoint: Self-Supervised Interest Point Detection and Description
  20 Dec 2017 36 repositories listed Syntology ran 11 of 11 samples · 0 unverified · 6 pointer-only (licence)
  This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision.
• Generalized End-to-End Loss for Speaker Verification
  28 Oct 2017 30 repositories listed Syntology ran 4 of 27 samples · 23 unverified · 1 pointer-only (licence)
  In this paper, we propose a new loss function called generalized end-to-end (GE2E) loss, which makes the training of speaker verification models more efficient than our previous tuple-based end-to-end (TE2E) loss…
• Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
  25 Jul 2018 25 repositories listed Syntology ran 4 of 16 samples · 12 unverified
  IBN-Net carefully integrates Instance Normalization (IN) and Batch Normalization (BN) as building blocks, and can be wrapped into many advanced deep networks to improve their performances.
• 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…
• Adversarial Discriminative Domain Adaptation
  17 Feb 2017 20 repositories listed Syntology ran 14 of 48 samples · 34 unverified · 12 pointer-only (licence)
  Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains.
• Learning to Adapt Structured Output Space for Semantic Segmentation
  28 Feb 2018 12 repositories listed Syntology ran 6 of 7 samples · 1 unverified · 7 pointer-only (licence)
  In this paper, we propose an adversarial learning method for domain adaptation in the context of semantic segmentation.
• Learning Generalisable Omni-Scale Representations for Person Re-Identification
  15 Oct 2019 9 repositories listed Syntology ran 6 of 9 samples · 3 unverified · 6 pointer-only (licence)
  An effective person re-identification (re-ID) model should learn feature representations that are both discriminative, for distinguishing similar-looking people, and generalisable, for deployment across datasets without…
• 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 from Simulated and Unsupervised Images through Adversarial Training
  22 Dec 2016 9 repositories listed Syntology ran 2 of 8 samples · 6 unverified
  With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations.
• Deep CORAL: Correlation Alignment for Deep Domain Adaptation
  6 Jul 2016 9 repositories listed Syntology ran 10 of 16 samples · 6 unverified · 10 pointer-only (licence)
  CORAL is a "frustratingly easy" unsupervised domain adaptation method that aligns the second-order statistics of the source and target distributions with a linear transformation.
• Domain Adaptation for Structured Output via Discriminative Patch Representations
  16 Jan 2019 8 repositories listed Syntology ran 4 of 4 samples · 0 unverified · 4 pointer-only (licence)
  Predicting structured outputs such as semantic segmentation relies on expensive per-pixel annotations to learn supervised models like convolutional neural networks.
• Domain Adaptive Faster R-CNN for Object Detection in the Wild
  8 Mar 2018 8 repositories listed Syntology ran 1 of 8 samples · 7 unverified · 4 pointer-only (licence)
  The results demonstrate the effectiveness of our proposed approach for robust object detection in various domain shift scenarios.
• Wasserstein Distance Guided Representation Learning for Domain Adaptation
  5 Jul 2017 8 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  Inspired by Wasserstein GAN, in this paper we propose a novel approach to learn domain invariant feature representations, namely Wasserstein Distance Guided Representation Learning (WDGRL).
• Unsupervised Image-to-Image Translation Networks
  2 Mar 2017 8 repositories listed Syntology ran 1 of 9 samples · 8 unverified · 1 pointer-only (licence)
  Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains.
• DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
  6 Oct 2013 8 repositories listed Syntology ran 1 of 7 samples · 6 unverified · 1 pointer-only (licence)
  We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re-purposed to novel generic…
• Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilities
  5 Sep 2024 7 repositories listed Syntology ran 5 of 5 samples · 0 unverified · 5 pointer-only (licence)
  The advancement of Large Language Models (LLMs) for domain applications in fields such as materials science and engineering depends on the development of fine-tuning strategies that adapt models for specialized,…
• Rescaling Egocentric Vision
  23 Jun 2020 7 repositories listed Syntology ran 3 of 18 samples · 15 unverified
  This paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS.
• Agnostic Federated Learning
  1 Feb 2019 7 repositories listed Syntology ran 3 of 10 samples · 7 unverified
  A key learning scenario in large-scale applications is that of federated learning, where a centralized model is trained based on data originating from a large number of clients.
• Multi-Task Deep Neural Networks for Natural Language Understanding
  31 Jan 2019 7 repositories listed Syntology ran 5 of 13 samples · 8 unverified · 2 pointer-only (licence)
  In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks.
• Diverse Image-to-Image Translation via Disentangled Representations
  2 Aug 2018 7 repositories listed
  Our model takes the encoded content features extracted from a given input and the attribute vectors sampled from the attribute space to produce diverse outputs at test time.
• Deep Hashing Network for Unsupervised Domain Adaptation
  22 Jun 2017 7 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  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.
• Deep Domain Confusion: Maximizing for Domain Invariance
  10 Dec 2014 7 repositories listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias on a standard benchmark.
• Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
  7 Sep 2022 6 repositories listed Syntology ran 6 of 8 samples · 2 unverified · 6 pointer-only (licence)
  The idea of rectified flow is to learn the ODE to follow the straight paths connecting the points drawn from \pi_0 and \pi_1 as much as possible.
• AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
  8 Jun 2021 6 repositories listed Syntology ran 8 of 20 samples · 12 unverified · 9 pointer-only (licence)
  We extend semi-supervised learning to the problem of domain adaptation to learn significantly higher-accuracy models that train on one data distribution and test on a different one.
• TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning
  14 Apr 2021 6 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  Learning sentence embeddings often requires a large amount of labeled data.
• Masked Language Model Scoring
  31 Oct 2019 6 repositories listed Syntology ran 5 of 10 samples · 5 unverified · 3 pointer-only (licence)
  Instead, we evaluate MLMs out of the box via their pseudo-log-likelihood scores (PLLs), which are computed by masking tokens one by one.
• Bridging Theory and Algorithm for Domain Adaptation
  11 Apr 2019 6 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  We introduce Margin Disparity Discrepancy, a novel measurement with rigorous generalization bounds, tailored to the distribution comparison with the asymmetric margin loss, and to the minimax optimization for easier…
• Multi-Adversarial Domain Adaptation
  4 Sep 2018 6 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Recent advances in deep domain adaptation reveal that adversarial learning can be embedded into deep networks to learn transferable features that reduce distribution discrepancy between the source and target domains.

Syntology lines on 29 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.