Synthetic-to-Real Translation

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

5 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.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 58 papers with code (68 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.

• 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.
• 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.
• 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.
• Virtual to Real Reinforcement Learning for Autonomous Driving
  13 Apr 2017 6 repositories listed
  To our knowledge, this is the first successful case of driving policy trained by reinforcement learning that can adapt to real world driving data.
• ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
  30 Nov 2018 4 repositories listed Syntology ran 1 of 3 samples · 2 unverified · 1 pointer-only (licence)
  Semantic segmentation is a key problem for many computer vision tasks.
• DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation
  29 Nov 2021 3 repositories listed Syntology ran 6 of 6 samples · 0 unverified · 6 pointer-only (licence)
  It improves the state of the art by 10.
• Bidirectional Learning for Domain Adaptation of Semantic Segmentation
  24 Apr 2019 3 repositories listed Syntology ran 3 of 8 samples · 5 unverified
  In this paper, we propose a novel bidirectional learning framework for domain adaptation of segmentation.
• CyCADA: Cycle-Consistent Adversarial Domain Adaptation
  8 Nov 2017 3 repositories listed Syntology ran 1 of 2 samples · 1 unverified
  Domain adaptation is critical for success in new, unseen environments.
• FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
  8 Dec 2016 3 repositories listed
  In this paper, we introduce the first domain adaptive semantic segmentation method, proposing an unsupervised adversarial approach to pixel prediction problems.
• Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation
  21 Nov 2023 2 repositories listed
  Based on such observation, we propose a depth-aware framework to explicitly leverage depth estimation to mix the categories and facilitate the two complementary tasks, i.
• Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation
  29 Mar 2021 2 repositories listed
  Domain adaptation is to transfer the shared knowledge learned from the source domain to a new environment, i.
• Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation
  26 Jan 2021 2 repositories listed Syntology ran 8 of 17 samples · 9 unverified
  In this paper, we rely on representative prototypes, the feature centroids of classes, to address the two issues for unsupervised domain adaptation.
• Instance Adaptive Self-Training for Unsupervised Domain Adaptation
  27 Aug 2020 2 repositories listed
  In this paper, we propose an instance adaptive self-training framework for UDA on the task of semantic segmentation.
• DACS: Domain Adaptation via Cross-domain Mixed Sampling
  17 Jul 2020 2 repositories listed Syntology ran 3 of 4 samples · 1 unverified · 3 pointer-only (licence)
  In this paper we address the problem of unsupervised domain adaptation (UDA), which attempts to train on labelled data from one domain (source domain), and simultaneously learn from unlabelled data in the domain of…
• Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
  16 Apr 2020 2 repositories listed Syntology ran 2 of 8 samples · 6 unverified
  Finally, to decrease the intra-domain gap, we propose to employ a self-supervised adaptation technique from the easy to the hard split.
• Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation
  8 Mar 2020 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 1 pointer-only (licence)
  This paper focuses on the unsupervised domain adaptation of transferring the knowledge from the source domain to the target domain in the context of semantic segmentation.
• Confidence Regularized Self-Training
  26 Aug 2019 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Recent advances in domain adaptation show that deep self-training presents a powerful means for unsupervised domain adaptation.
• A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes
  24 Dec 2018 2 repositories listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  Hence, we propose a curriculum-style learning approach to minimizing the domain gap in urban scene semantic segmentation.
• Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation
  1 Jul 2023 1 repository listed
  Unlike domain gap challenges, USSS is unique in that the semantic categories are often similar in different urban scenes, while the styles can vary significantly due to changes in urban landscapes, weather conditions,…
• Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation
  14 Feb 2023 1 repository listed
  The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models.
• MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
  2 Dec 2022 1 repository listed
  MIC significantly improves the state-of-the-art performance across the different recognition tasks for synthetic-to-real, day-to-nighttime, and clear-to-adverse-weather UDA.
• ELDA: Using Edges to Have an Edge on Semantic Segmentation Based UDA
  16 Nov 2022 1 repository listed
  Despite their effectiveness, using depth as domain invariant information in UDA tasks may lead to multiple issues, such as excessively high extraction costs and difficulties in achieving a reliable prediction quality.
• PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation
  14 Nov 2022 1 repository listed
  In an attempt to fill this gap, we propose a unified pixel- and patch-wise self-supervised learning framework, called PiPa, for domain adaptive semantic segmentation that facilitates intra-image pixel-wise correlations…
• Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation
  16 Sep 2022 1 repository listed
  In this work, we resort to data mixing to establish a deliberated domain bridging (DDB) for DASS, through which the joint distributions of source and target domains are aligned and interacted with each in the…
• CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic Segmentation
  27 Aug 2022 1 repository listed
  In this work, we propose CLUDA, a simple, yet novel method for performing unsupervised domain adaptation (UDA) for semantic segmentation by incorporating contrastive losses into a student-teacher learning paradigm, that…
• Exploring High-quality Target Domain Information for Unsupervised Domain Adaptive Semantic Segmentation
  12 Aug 2022 1 repository listed
  Such a strategy can generate the object boundaries in target domain (edge of target-domain object areas) with the correct labels.
• HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
  27 Apr 2022 1 repository listed
  Therefore, we propose HRDA, a multi-resolution training approach for UDA, that combines the strengths of small high-resolution crops to preserve fine segmentation details and large low-resolution crops to capture…
• ProCST: Boosting Semantic Segmentation Using Progressive Cyclic Style-Transfer
  25 Apr 2022 1 repository listed
  This new data has a reduced domain gap from the desired target domain, which facilitates the applied UDA approach to close the gap further.
• SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation
  19 Apr 2022 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Domain adaptive semantic segmentation attempts to make satisfactory dense predictions on an unlabeled target domain by utilizing the supervised model trained on a labeled source domain.
• Bidirectional Self-Training with Multiple Anisotropic Prototypes for Domain Adaptive Semantic Segmentation
  16 Apr 2022 1 repository listed
  A thriving trend for domain adaptive segmentation endeavors to generate the high-quality pseudo labels for target domain and retrain the segmentor on them.

Syntology lines on 13 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.