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 451475 of 1751 papers

TitleStatusHype
SWAD: Domain Generalization by Seeking Flat MinimaCode1
Domain Invariant Representation Learning with Domain Density TransformationsCode1
Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentationCode1
Style Normalization and Restitution for Domain Generalization and AdaptationCode1
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic AugmentationCode1
MASKER: Masked Keyword Regularization for Reliable Text ClassificationCode1
Towards Recognizing New Semantic Concepts in New Visual DomainsCode1
Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-IdentificationCode1
Domain Generalization via Entropy RegularizationCode1
Meta Batch-Instance Normalization for Generalizable Person Re-IdentificationCode1
Environment Inference for Invariant LearningCode1
Matching-space Stereo Networks for Cross-domain GeneralizationCode1
Unseen Target Stance Detection with Adversarial Domain GeneralizationCode1
Permuted AdaIN: Reducing the Bias Towards Global Statistics in Image ClassificationCode1
Domain Generalization for Medical Imaging Classification with Linear-Dependency RegularizationCode1
Learning to Balance Specificity and Invariance for In and Out of Domain GeneralizationCode1
Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical ImagingCode1
Informative Dropout for Robust Representation Learning: A Shape-bias PerspectiveCode1
Dual Distribution Alignment Network for Generalizable Person Re-IdentificationCode1
Robust and Generalizable Visual Representation Learning via Random ConvolutionsCode1
Towards Recognizing Unseen Categories in Unseen DomainsCode1
Learning to Generate Novel Domains for Domain GeneralizationCode1
Adaptive Risk Minimization: Learning to Adapt to Domain ShiftCode1
DART: Open-Domain Structured Data Record to Text GenerationCode1
Self-Challenging Improves Cross-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