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

Showing 15761600 of 1751 papers

TitleStatusHype
Learning to Balance Specificity and Invariance for In and Out of Domain GeneralizationCode1
Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information0
Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical ImagingCode1
Zero Shot Domain GeneralizationCode0
Informative Dropout for Robust Representation Learning: A Shape-bias PerspectiveCode1
Improving Explainability of Image Classification in Scenarios with Class Overlap: Application to COVID-19 and Pneumonia0
Prompt Agnostic Essay Scorer: A Domain Generalization Approach to Cross-prompt Automated Essay ScoringCode0
SimPose: Effectively Learning DensePose and Surface Normals of People from Simulated Data0
SimPose: Effectively Learning DensePose and Surface Normals of People from Simulated Data0
Discrepancy Minimization in Domain Generalization with Generative Nearest Neighbors0
Dual Distribution Alignment Network for Generalizable Person Re-IdentificationCode1
Robust and Generalizable Visual Representation Learning via Random ConvolutionsCode1
Self-Supervised Learning Across Domains0
Towards Recognizing Unseen Categories in Unseen DomainsCode1
Domain Generalization via Optimal Transport with Metric Similarity Learning0
Accounting for Unobserved Confounding in Domain Generalization0
Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization0
Learning to Learn with Variational Information Bottleneck for Domain Generalization0
Untapped Potential of Data Augmentation: A Domain Generalization Viewpoint0
Learning to Generate Novel Domains for Domain GeneralizationCode1
DART: Open-Domain Structured Data Record to Text GenerationCode1
Adaptive Risk Minimization: Learning to Adapt to Domain ShiftCode1
Self-Challenging Improves Cross-Domain GeneralizationCode1
Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen DomainsCode1
In Search of Lost Domain GeneralizationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SIMPLE+Average Accuracy99Unverified
2PromptStyler (CLIP, ViT-L/14)Average Accuracy98.6Unverified
3GMDG (RegNetY-16GF, SWAD)Average Accuracy97.9Unverified
4D-Triplet(RegNetY-16GF)Average Accuracy97.6Unverified
5MoA (OpenCLIP, ViT-B/16)Average Accuracy97.4Unverified
6GMDG (e RegNetY-16GF)Average Accuracy97.3Unverified
7PromptStyler (CLIP, ViT-B/16)Average Accuracy97.2Unverified
8SPG (CLIP, ViT-B/16)Average Accuracy97Unverified
9CAR-FT (CLIP, ViT-B/16)Average Accuracy96.8Unverified
10MIRO (RegNetY-16GF, SWAD)Average Accuracy96.8Unverified
#ModelMetricClaimedVerifiedStatus
1ViT-8/B-224Accuracy - Clean Images450Unverified
2VOLO-D5Accuracy - All Images57.2Unverified
3ConvNeXt-BAccuracy - All Images53.5Unverified
4ResNeXt-101 32x16dAccuracy - All Images51.7Unverified
5EfficientNet-B8 (advprop+autoaug)Accuracy - All Images50.5Unverified
6EfficientNet-B7 (advprop+autoaug)Accuracy - All Images49.7Unverified
7EfficientNet-B6 (advprop+autoaug)Accuracy - All Images49.6Unverified
8EfficientNet-B5 (advprop+autoaug)Accuracy - All Images49.1Unverified
9ViT-16/L-224Accuracy - All Images49Unverified
10ResNet-50 (gn)Accuracy - All Images48.9Unverified