SOTAVerified

Domain Generalization

The idea of Domain Generalization is to learn from one or multiple training domains, to extract a domain-agnostic model which can be applied to an unseen domain

Source: Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning

Papers

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Showing 451–475 of 1751 papers

TitleStatusHype
Complex Style Image Transformations for Domain Generalization in Medical Images—0
Domain generalization for retinal vessel segmentation via Hessian-based vector field—0
Towards a Better Evaluation of Out-of-Domain Generalization—0
Lifelong Learning Using a Dynamically Growing Tree of Sub-networks for Domain Generalization in Video Object Segmentation—0
Domain-Inspired Sharpness-Aware Minimization Under Domain ShiftsCode0
WIDIn: Wording Image for Domain-Invariant Representation in Single-Source Domain Generalization—0
Federated Unsupervised Domain Generalization using Global and Local Alignment of GradientsCode1
Continuous Temporal Domain GeneralizationCode2
Grounding Stylistic Domain Generalization with Quantitative Domain Shift Measures and Synthetic Scene ImagesCode0
NuwaTS: a Foundation Model Mending Every Incomplete Time Series—0
Unbiased Faster R-CNN for Single-source Domain Generalized Object Detection—0
Exploring the Impact of Synthetic Data for Aerial-view Human Detection—0
Improving Single Domain-Generalized Object Detection: A Focus on Diversification and AlignmentCode1
Comparative Analysis of Different Efficient Fine Tuning Methods of Large Language Models (LLMs) in Low-Resource Setting—0
RobustMVS: Single Domain Generalized Deep Multi-view StereoCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
Benchmarking Cross-Domain Audio-Visual Deception Detection—0
Non-stationary Domain Generalization: Theory and Algorithm—0
PhysMLE: Generalizable and Priors-Inclusive Multi-task Remote Physiological Measurement—0
All in One Framework for Multimodal Re-identification in the Wild—0
Joint semi-supervised and contrastive learning enables domain generalization and multi-domain segmentation—0
Adapting to Distribution Shift by Visual Domain Prompt GenerationCode1
Improving Domain Generalization on Gaze Estimation via Branch-out Auxiliary Regularization—0
RaffeSDG: Random Frequency Filtering enabled Single-source Domain Generalization for Medical Image SegmentationCode1
UniGen: Universal Domain Generalization for Sentiment Classification via Zero-shot Dataset GenerationCode0
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All datasetsPACSVizWiz-ClassificationImageNet-COffice-HomeImageNet-AImageNet-RDomainNetVLCSTerraIncognitaGTA-to-Avg(Cityscapes,BDD,Mapillary)ImageNet-SketchGTA5 to Cityscapes

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SIMPLE+Average Accuracy99—Unverified
2PromptStyler (CLIP, ViT-L/14)Average Accuracy98.6—Unverified
3GMDG (RegNetY-16GF, SWAD)Average Accuracy97.9—Unverified
4D-Triplet(RegNetY-16GF)Average Accuracy97.6—Unverified
5MoA (OpenCLIP, ViT-B/16)Average Accuracy97.4—Unverified
6GMDG (e RegNetY-16GF)Average Accuracy97.3—Unverified
7PromptStyler (CLIP, ViT-B/16)Average Accuracy97.2—Unverified
8SPG (CLIP, ViT-B/16)Average Accuracy97—Unverified
9CAR-FT (CLIP, ViT-B/16)Average Accuracy96.8—Unverified
10MIRO (RegNetY-16GF, SWAD)Average Accuracy96.8—Unverified
#ModelMetricClaimedVerifiedStatus
1ViT-8/B-224Accuracy - Clean Images450—Unverified
2VOLO-D5Accuracy - All Images57.2—Unverified
3ConvNeXt-BAccuracy - All Images53.5—Unverified
4ResNeXt-101 32x16dAccuracy - All Images51.7—Unverified
5EfficientNet-B8 (advprop+autoaug)Accuracy - All Images50.5—Unverified
6EfficientNet-B7 (advprop+autoaug)Accuracy - All Images49.7—Unverified
7EfficientNet-B6 (advprop+autoaug)Accuracy - All Images49.6—Unverified
8EfficientNet-B5 (advprop+autoaug)Accuracy - All Images49.1—Unverified
9ViT-16/L-224Accuracy - All Images49—Unverified
10ResNet-50 (gn)Accuracy - All Images48.9—Unverified