Domain Generalization

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

21 leaderboard tables shown for this task, 21 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 21 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

31 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 31 until expanded.

Subtasks archive 2025-07-28

3 subtasks in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 859 papers with code (1,751 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.

• Deep Residual Learning for Image Recognition
  10 Dec 2015 484 repositories listed Syntology ran 230 of 377 samples · 147 unverified · 187 pointer-only (licence)
  Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
• Very Deep Convolutional Networks for Large-Scale Image Recognition
  4 Sep 2014 305 repositories listed Syntology ran 12 of 122 samples · 110 unverified · 4 pointer-only (licence)
  In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting.
• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
  28 May 2019 144 repositories listed Syntology ran 171 of 302 samples · 131 unverified · 112 pointer-only (licence)
  Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available.
• mixup: Beyond Empirical Risk Minimization
  25 Oct 2017 71 repositories listed Syntology ran 30 of 47 samples · 17 unverified · 15 pointer-only (licence)
  We also find that mixup reduces the memorization of corrupt labels, increases the robustness to adversarial examples, and stabilizes the training of generative adversarial networks.
• Aggregated Residual Transformations for Deep Neural Networks
  16 Nov 2016 61 repositories listed Syntology ran 34 of 80 samples · 46 unverified · 13 pointer-only (licence)
  Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set.
• Masked Autoencoders Are Scalable Vision Learners
  11 Nov 2021 58 repositories listed Syntology ran 71 of 137 samples · 66 unverified · 73 pointer-only (licence)
  Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels.
• A ConvNet for the 2020s
  10 Jan 2022 54 repositories listed Syntology ran 54 of 80 samples · 26 unverified · 11 pointer-only (licence)
  The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model.
• 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…
• AutoAugment: Learning Augmentation Policies from Data
  24 May 2018 33 repositories listed Syntology ran 6 of 43 samples · 37 unverified · 2 pointer-only (licence)
  In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch.
• CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
  13 May 2019 30 repositories listed Syntology ran 17 of 24 samples · 7 unverified · 5 pointer-only (licence)
  Regional dropout strategies have been proposed to enhance the performance of convolutional neural network classifiers.
• Bag of Tricks for Image Classification with Convolutional Neural Networks
  4 Dec 2018 28 repositories listed Syntology ran 4 of 15 samples · 11 unverified · 5 pointer-only (licence)
  Much of the recent progress made in image classification research can be credited to training procedure refinements, such as changes in data augmentations and optimization methods.
• Improved Regularization of Convolutional Neural Networks with Cutout
  15 Aug 2017 28 repositories listed Syntology ran 21 of 24 samples · 3 unverified · 5 pointer-only (licence)
  Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks.
• DINOv2: Learning Robust Visual Features without Supervision
  14 Apr 2023 26 repositories listed Syntology ran 21 of 46 samples · 25 unverified · 12 pointer-only (licence)
  The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision.
• 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.
• RandAugment: Practical automated data augmentation with a reduced search space
  30 Sep 2019 19 repositories listed Syntology ran 58 of 65 samples · 7 unverified · 17 pointer-only (licence)
  Additionally, due to the separate search phase, these approaches are unable to adjust the regularization strength based on model or dataset size.
• Learning to Prompt for Vision-Language Models
  2 Sep 2021 18 repositories listed Syntology ran 3 of 12 samples · 9 unverified
  Large pre-trained vision-language models like CLIP have shown great potential in learning representations that are transferable across a wide range of downstream tasks.
• Invariant Risk Minimization
  5 Jul 2019 18 repositories listed Syntology ran 18 of 30 samples · 12 unverified · 10 pointer-only (licence)
  We introduce Invariant Risk Minimization (IRM), a learning paradigm to estimate invariant correlations across multiple training distributions.
• AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
  5 Dec 2019 15 repositories listed Syntology ran 43 of 51 samples · 8 unverified · 25 pointer-only (licence)
  We propose AugMix, a data processing technique that is simple to implement, adds limited computational overhead, and helps models withstand unforeseen corruptions.
• ResNet strikes back: An improved training procedure in timm
  1 Oct 2021 14 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  We share competitive training settings and pre-trained models in the timm open-source library, with the hope that they will serve as better baselines for future work.
• Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
  28 Mar 2019 14 repositories listed Syntology ran 2 of 3 samples · 1 unverified
  Then we propose a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations.
• A Closer Look at Few-shot Classification
  8 Apr 2019 13 repositories listed Syntology ran 7 of 8 samples · 1 unverified · 7 pointer-only (licence)
  Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples.
• Conditional Prompt Learning for Vision-Language Models
  10 Mar 2022 12 repositories listed Syntology ran 4 of 6 samples · 2 unverified
  With the rise of powerful pre-trained vision-language models like CLIP, it becomes essential to investigate ways to adapt these models to downstream datasets.
• In Search of Lost Domain Generalization
  2 Jul 2020 12 repositories listed Syntology ran 3 of 7 samples · 4 unverified · 1 pointer-only (licence)
  As a first step, we realize that model selection is non-trivial for domain generalization tasks.
• On the limits of cross-domain generalization in automated X-ray prediction
  6 Feb 2020 9 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  This large scale study focuses on quantifying what X-rays diagnostic prediction tasks generalize well across multiple different datasets.
• 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.
• MetaFormer Baselines for Vision
  24 Oct 2022 8 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  By simply applying depthwise separable convolutions as token mixer in the bottom stages and vanilla self-attention in the top stages, the resulting model CAFormer sets a new record on ImageNet-1K: it achieves an…
• Self-Challenging Improves Cross-Domain Generalization
  5 Jul 2020 8 repositories listed Syntology ran 3 of 7 samples · 4 unverified
  We introduce a simple training heuristic, Representation Self-Challenging (RSC), that significantly improves the generalization of CNN to the out-of-domain data.
• Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization
  20 Nov 2019 8 repositories listed Syntology ran 9 of 10 samples · 1 unverified · 2 pointer-only (licence)
  Distributionally robust optimization (DRO) allows us to learn models that instead minimize the worst-case training loss over a set of pre-defined groups.
• VOLO: Vision Outlooker for Visual Recognition
  24 Jun 2021 7 repositories listed Syntology ran 1 of 6 samples · 5 unverified
  Though recently the prevailing vision transformers (ViTs) have shown great potential of self-attention based models in ImageNet classification, their performance is still inferior to that of the latest SOTA CNNs if no…
• Making Convolutional Networks Shift-Invariant Again
  25 Apr 2019 7 repositories listed Syntology ran 2 of 4 samples · 2 unverified · 3 pointer-only (licence)
  The well-known signal processing fix is anti-aliasing by low-pass filtering before downsampling.

Syntology lines on 30 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.